Visualization of publically available papers presented at ICCV 2013
Hover over a node to see the paper title. Click on a color to only show papers connected to that cluster. Zoom and move around with normal map controls.
Papers are linked together based on TF-IDF similarity and are colored using their predicted topic index.
Toggle the topics below to sort by category. The top 10 words from each cluster are shown.
|
Compensating for Motion during Direct-Global Separation [pdf]
Supreeth Achar, Stephen T. Nuske, Srinivasa G. Narasimhan |
|
Abstract: Separating the direct and global components of radiance can aid shape recovery algorithms and can provide useful information about materials in a scene. Practical methods for finding the direct and global components use multiple images captured under varying illumination patterns and require the scene, light source and camera to remain sta- tionary during the image acquisition process. In this pa- per, we develop a motion compensation method that relaxes this condition and allows direct-global separation to be per- formed on video sequences of dynamic scenes captured by moving projector-camera systems. Key to our method is be- ing able to register frames in a video sequence to each other in the presence of time varying, high frequency active illu- mination patterns. We compare our motion compensated method to alternatives such as single shot separation and frame interleaving as well as ground truth. We present re- sults on challenging video sequences that include various types of motions and deformations in scenes that contain complex materials like fabric, skin, leaves and wax.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: We consider the problem of estimating the extrinsic pa- rameters (pose) of a camera with respect to a reference 3D object without a direct view. Since the camera does not view the object directly, previous approaches have utilized reflec- tions in a planar mirror to solve this problem. However, a planar mirror based approach requires a minimum of three reflections and has degenerate configurations where esti- mation fails. In this paper, we show that the pose can be obtained using a single reflection in a spherical mirror of known radius. This makes our approach simpler and easier in practice. In addition, unlike planar mirrors, the spher- ical mirror based approach does not have any degenerate configurations, leading to a robust algorithm.
While a planar mirror reflection results in a virtual per- spective camera, a spherical mirror reflection results in a non-perspective axial camera. The axial nature of rays al- lows us to compute the axis (direction of sphere center) and few pose parameters in a linear fashion. We then derive an analytical solution to obtain the distance to the sphere cen- ter and remaining pose parameters and show that it corre- sponds to solving a 16th degree equation. We present com- parisons with a recent method that use planar mirrors and show that our approach recovers more accurate pose in the presence of noise. Extensive simulations and results on real data validate our algorithm.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Random Grids: Fast Approximate Nearest Neighbors and Range Searching for Image Search [pdf]
Dror Aiger, Efi Kokiopoulou, Ehud Rivlin |
|
Abstract: We propose two solutions for both nearest neigh- bors and range search problems. For the nearest neighbors problem, we propose a c-approximate so- lution for the restricted version of the decision prob- lem with bounded radius which is then reduced to the nearest neighbors by a known reduction. For range searching we propose a scheme that learns the parameters in a learning stage adopting them to the case of a set of points with low intrinsic dimen- sion that are embedded in high dimensional space (common scenario for image point descriptors). We compare our algorithms to the best known methods for these problems, i.e. LSH, ANN and FLANN. We show analytically and experimentally that we can do better for moderate approximation factor. Our algorithms are trivial to parallelize. In the experi- ments conducted, running on couple of million im- ages, our algorithms show meaningful speed-ups when compared with the above mentioned methods.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Pose Estimation and Segmentation of People in 3D Movies [pdf]
Karteek Alahari, Guillaume Seguin, Josef Sivic, Ivan Laptev |
|
Abstract: We seek to obtain a pixel-wise segmentation and pose estimation of multiple people in a stereoscopic video. This involves challenges such as dealing with unconstrained stereoscopic video, non-stationary cameras, and complex indoor and outdoor dynamic scenes. The contributions of our work are two-fold: First, we develop a segmentation model incorporating person detection, pose estimation, as well as colour, motion, and disparity cues. Our new model explicitly represents depth ordering and occlusion. Second, we introduce a stereoscopic dataset with frames extracted from feature-length movies StreetDance 3D and Pina. The dataset contains 2727 realistic stereo pairs and in- cludes annotation of human poses, person bounding boxes, and pixel-wise segmentations for hundreds of people. The dataset is composed of indoor and outdoor scenes depicting multiple people with frequent occlusions. We demonstrate results on our new challenging dataset, as well as on the H2view dataset from (Sheasby et al. ACCV 2012).
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: In this paper a notion of flow complexity that measures the amount of interaction among objects is introduced and an approach to compute it directly from a video sequence is proposed. The approach employs particle trajectories as the input representation of motion and maps it into a braid based representation. The mapping is based on the obser- vation that 2D trajectories of particles take the form of a braid in space-time due to the intermingling among parti- cles over time. As a result of this mapping, the problem of estimating the flow complexity from particle trajectories be- comes the problem of estimating braid complexity, which in turn can be computed by measuring the topological entropy of a braid. For this purpose recently developed mathemati- cal tools from braid theory are employed which allow rapid computation of topological entropy of braids. The approach is evaluated on a dataset consisting of open source videos depicting variations in terms of types of moving objects, scene layout, camera view angle, motion patterns, and ob- ject densities. The results show that the proposed approach is able to quantify the complexity of the flow, and at the same time provides useful insights about the sources of the complexity.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Handwritten Word Spotting with Corrected Attributes [pdf]
Jon Almazan, Albert Gordo, Alicia Fornes, Ernest Valveny |
|
Abstract: We propose an approach to multi-writer word spotting, where the goal is to find a query word in a dataset com- prised of document images. We propose an attributes-based approach that leads to a low-dimensional, fixed-length rep- resentation of the word images that is fast to compute and, especially, fast to compare. This approach naturally leads to an unified representation of word images and strings, which seamlessly allows one to indistinctly perform query- by-example, where the query is an image, and query-by- string, where the query is a string. We also propose a cal- ibration scheme to correct the attributes scores based on Canonical Correlation Analysis that greatly improves the results on a challenging dataset. We test our approach on two public datasets showing state-of-the-art results.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Calibration-Free Gaze Estimation Using Human Gaze Patterns [pdf]
Fares Alnajar, Theo Gevers, Roberto Valenti, Sennay Ghebreab |
|
Abstract: We present a novel method to auto-calibrate gaze esti- mators based on gaze patterns obtained from other viewers. Our method is based on the observation that the gaze pat- terns of humans are indicative of where a new viewer will look at [12]. When a new viewer is looking at a stimu- lus, we first estimate a topology of gaze points (initial gaze points). Next, these points are transformed so that they match the gaze patterns of other humans to find the correct gaze points.
In a flexible uncalibrated setup with a web camera and no chin rest, the proposed method was tested on ten sub- jects and ten images. The method estimates the gaze points after looking at a stimulus for a few seconds with an aver- age accuracy of 4.3. Although the reported performance is lower than what could be achieved with dedicated hard- ware or calibrated setup, the proposed method still provides a sufficient accuracy to trace the viewer attention. This is promising considering the fact that auto-calibration is done in a flexible setup , without the use of a chin rest, and based only on a few seconds of gaze initialization data. To the best of our knowledge, this is the first work to use human gaze patterns in order to auto-calibrate gaze estimators.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Monte Carlo Tree Search for Scheduling Activity Recognition [pdf]
Mohamed R. Amer, Sinisa Todorovic, Alan Fern, Song-Chun Zhu |
|
Abstract: This paper presents an efficient approach to video pars- ing. Our videos show a number of co-occurring individ- ual and group activities. To address challenges of the do- main, we use an expressive spatiotemporal AND-OR graph (ST-AOG) that jointly models activity parts, their spatiotem- poral relations, and context, as well as enables multitarget tracking. The standard ST-AOG inference is prohibitively expensive in our setting, since it would require running a multitude of detectors, and tracking their detections in a long video footage. This problem is addressed by for- mulating a cost-sensitive inference of ST-AOG as Monte Carlo Tree Search (MCTS). For querying an activity in the video, MCTS optimally schedules a sequence of detectors and trackers to be run, and where they should be applied in the space-time volume. Evaluation on the benchmark datasets demonstrates that MCTS enables two-magnitude speed-ups without compromising accuracy relative to the standard cost-insensitive inference.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: The task of object pose estimation has been a challenge since the early days of computer vision. To estimate the pose (or viewpoint) of an object, people have mostly looked at object intrinsic features, such as shape or appearance. Surprisingly, informative features provided by other, exter- nal elements in the scene, have so far mostly been ignored. At the same time, contextual cues have been shown to be of great benefit for related tasks such as object detection or action recognition. In this paper, we explore how in- formation from other objects in the scene can be exploited for pose estimation. In particular, we look at object con- figurations. We show that, starting from noisy object de- tections and pose estimates, exploiting the estimated pose and location of other objects in the scene can help to esti- mate the objects poses more accurately. We explore both a camera-centered as well as an object-centered represen- tation for relations. Experiments on the challenging KITTI dataset show that object configurations can indeed be used as a complementary cue to appearance-based pose estima- tion. In addition, object-centered relational representations can also assist object detection.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Revisiting Example Dependent Cost-Sensitive Learning with Decision Trees [pdf]
Oisin Mac Aodha, Gabriel J. Brostow |
|
Abstract: Typical approaches to classification treat class labels as disjoint. For each training example, it is assumed that there is only one class label that correctly describes it, and that all other labels are equally bad. We know however, that good and bad labels are too simplistic in many scenarios, hurting accuracy. In the realm of example dependent cost- sensitive learning, each label is instead a vector represent- ing a data points affinity for each of the classes. At test time, our goal is not to minimize the misclassification rate, but to maximize that affinity. We propose a novel exam- ple dependent cost-sensitive impurity measure for decision trees. Our experiments show that this new impurity measure improves test performance while still retaining the fast test times of standard classification trees. We compare our ap- proach to classification trees and other cost-sensitive meth- ods on three computer vision problems, tracking, descriptor matching, and optical flow, and show improvements in all three domains.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: This paper addresses the data assignment problem in multi frame multi object tracking in video sequences. Traditional methods employing maximum weight bipar- tite matching offer limited temporal modeling. It has re- cently been shown [6, 8, 24] that incorporating higher or- der temporal constraints improves the assignment solution. Finding maximum weight matching with higher order con- straints is however NP-hard and the solutions proposed un- til now have either been greedy [8] or rely on greedy round- ing of the solution obtained from spectral techniques [15]. We propose a novel algorithm to find the approximate solu- tion to data assignment problem with higher order temporal constraints using the method of dual decomposition and the MPLP message passing algorithm [21]. We compare the proposed algorithm with an implementation of [8] and [15] and show that proposed technique provides better solution with a bound on approximation factor for each inferred so- lution.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Quantize and Conquer: A Dimensionality-Recursive Solution to Clustering, Vector Quantization, and Image Retrieval [pdf]
Yannis Avrithis |
|
Abstract: Inspired by the close relation between nearest neighbor search and clustering in high-dimensional spaces as well as the success of one helping to solve the other, we introduce a new paradigm where both problems are solved simultane- ously. Our solution is recursive, not in the size of input data but in the number of dimensions. One result is a cluster- ing algorithm that is tuned to small codebooks but does not need all data in memory at the same time and is practically constant in the data size. As a by-product, a tree struc- ture performs either exact or approximate quantization on trained centroids, the latter being not very precise but ex- tremely fast. A lesser contribution is a new indexing scheme for image retrieval that exploits multiple small codebooks to provide an arbitrarily fine partition of the descriptor space. Large scale experiments on public datasets exhibit state of the art performance and remarkable generalization.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Finding Causal Interactions in Video Sequences [pdf]
Mustafa Ayazoglu, Burak Yilmaz, Mario Sznaier, Octavia Camps |
|
Abstract: This paper considers the problem of detecting causal in- teractions in video clips. Specifically, the goal is to detect whether the actions of a given target can be explained in terms of the past actions of a collection of other agents. We propose to solve this problem by recasting it into a directed graph topology identification, where each node corresponds to the observed motion of a given target, and each link in- dicates the presence of a causal correlation. As shown in the paper, this leads to a block-sparsification problem that can be efficiently solved using a modified Group-Lasso type approach, capable of handling missing data and outliers (due for instance to occlusion and mis-identified correspon- dences). Moreover, this approach also identifies time in- stants where the interactions between agents change, thus providing event detection capabilities. These results are il- lustrated with several examples involving nontrivial inter- actions amongst several human subjects.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Randomized Ensemble Tracking [pdf]
Qinxun Bai, Zheng Wu, Stan Sclaroff, Margrit Betke, Camille Monnier |
|
Abstract: We propose a randomized ensemble algorithm to model the time-varying appearance of an object for visual track- ing. In contrast with previous online methods for updat- ing classifier ensembles in tracking-by-detection, the weight vector that combines weak classifiers is treated as a ran- dom variable and the posterior distribution for the weight vector is estimated in a Bayesian manner. In essence, the weight vector is treated as a distribution that reflects the confidence among the weak classifiers used to construct and adapt the classifier ensemble. The resulting formula- tion models the time-varying discriminative ability among weak classifiers so that the ensembled strong classifier can adapt to the varying appearance, backgrounds, and occlu- sions. The formulation is tested in a tracking-by-detection implementation. Experiments on 28 challenging benchmark videos demonstrate that the proposed method can achieve results comparable to and often better than those of state- of-the-art approaches.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Unsupervised Domain Adaptation by Domain Invariant Projection [pdf]
Mahsa Baktashmotlagh, Mehrtash T. Harandi, Brian C. Lovell, Mathieu Salzmann |
|
Abstract: Domain-invariant representations are key to addressing the domain shift problem where the training and test exam- ples follow different distributions. Existing techniques that have attempted to match the distributions of the source and target domains typically compare these distributions in the original feature space. This space, however, may not be di- rectly suitable for such a comparison, since some of the fea- tures may have been distorted by the domain shift, or may be domain specific. In this paper, we introduce a Domain Invariant Projection approach: An unsupervised domain adaptation method that overcomes this issue by extracting the information that is invariant across the source and tar- get domains. More specifically, we learn a projection of the data to a low-dimensional latent space where the distance between the empirical distributions of the source and target examples is minimized. We demonstrate the effectiveness of our approach on the task of visual object recognition and show that it outperforms state-of-the-art methods on a stan- dard domain adaptation benchmark dataset.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Space-Time Robust Representation for Action Recognition [pdf]
Nicolas Ballas, Yi Yang, Zhen-Zhong Lan, Bertrand Delezoide, Francoise Preteux, Alexander Hauptmann |
|
Abstract: We address the problem of action recognition in uncon- strained videos. We propose a novel content driven pool- ing that leverages space-time context while being robust to- ward global space-time transformations. Being robust to such transformations is of primary importance in uncon- strained videos where the action localizations can drasti- cally shift between frames. Our pooling identifies regions of interest using video structural cues estimated by differ- ent saliency functions. To combine the different structural information, we introduce an iterative structure learning al- gorithm, WSVM (weighted SVM), that determines the opti- mal saliency layout of an action model through a sparse reg- ularizer. A new optimization method is proposed to solve the WSVM highly non-smooth objective function. We evaluate our approach on standard action datasets (KTH, UCF50 and HMDB). Most noticeably, the accuracy of our algo- rithm reaches 51.8% on the challenging HMDB dataset which outperforms the state-of-the-art of 7.3% relatively.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: Collecting and annotating videos of realistic human ac- tions is tedious, yet critical for training action recognition systems. We propose a method to actively request the most useful video annotations among a large set of unlabeled videos. Predicting the utility of annotating unlabeled video is not trivial, since any given clip may contain multiple ac- tions of interest, and it need not be trimmed to temporal regions of interest. To deal with this problem, we propose a detection-based active learner to train action category models. We develop a voting-based framework to local- ize likely intervals of interest in an unlabeled clip, and use them to estimate the total reduction in uncertainty that an- notating that clip would yield. On three datasets, we show our approach can learn accurate action detectors more effi- ciently than alternative active learning strategies that fail to accommodate the untrimmed nature of real video data.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Fast Sparsity-Based Orthogonal Dictionary Learning for Image Restoration [pdf]
Chenglong Bao, Jian-Feng Cai, Hui Ji |
|
Abstract: In recent years, how to learn a dictionary from input im- ages for sparse modelling has been one very active topic in image processing and recognition. Most existing dic- tionary learning methods consider an over-complete dic- tionary, e.g. the K-SVD method. Often they require solv- ing some minimization problem that is very challenging in terms of computational feasibility and efficiency. How- ever, if the correlations among dictionary atoms are not well constrained, the redundancy of the dictionary does not necessarily improve the performance of sparse coding. This paper proposed a fast orthogonal dictionary learning method for sparse image representation. With comparable performance on several image restoration tasks, the pro- posed method is much more computationally efficient than the over-complete dictionary based learning methods.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Fast High Dimensional Vector Multiplication Face Recognition [pdf]
Oren Barkan, Jonathan Weill, Lior Wolf, Hagai Aronowitz |
|
Abstract: This paper advances descriptor-based face recognition by suggesting a novel usage of descriptors to form an over-complete representation, and by proposing a new metric learning pipeline within the same/not-same framework. First, the Over-Complete Local Binary Patterns (OCLBP) face representation scheme is introduced as a multi-scale modified version of the Local Binary Patterns (LBP) scheme. Second, we propose an efficient matrix-vector multiplication-based recognition system. The system is based on Linear Discriminant Analysis (LDA) coupled with Within Class Covariance Normalization (WCCN). This is further extended to the unsupervised case by proposing an unsupervised variant of WCCN. Lastly, we introduce Diffusion Maps (DM) for non-linear dimensionality reduction as an alternative to the Whitened Principal Component Analysis (WPCA) method which is often used in face recognition.
We evaluate the proposed framework on the LFW face recognition dataset under the restricted, unrestricted and unsupervised protocols. In all three cases we achieve very competitive results.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Volumetric Semantic Segmentation Using Pyramid Context Features [pdf]
Jonathan T. Barron, Mark D. Biggin, Pablo Arbelaez, David W. Knowles, Soile V.E. Keranen, Jitendra Malik |
|
Abstract: We present an algorithm for the per-voxel semantic seg- mentation of a three-dimensional volume. At the core of our algorithm is a novel pyramid context feature, a de- scriptive representation designed such that exact per-voxel linear classification can be made extremely efficient. This feature not only allows for efficient semantic segmentation but enables other aspects of our algorithm, such as novel learned features and a stacked architecture that can reason about self-consistency. We demonstrate our technique on 3D fluorescence microscopy data of Drosophila embryos for which we are able to produce extremely accurate semantic segmentations in a matter of minutes, and for which other algorithms fail due to the size and high-dimensionality of the data, or due to the difficulty of the task.
|
Similar papers:
[rank all papers by similarity to this]
|
|
A Robust Analytical Solution to Isometric Shape-from-Template with Focal Length Calibration [pdf]
Adrien Bartoli, Daniel Pizarro, Toby Collins |
|
Abstract: We study the uncalibrated isometric Shape-from- Template problem, that consists in estimating an isometric deformation from a template shape to an input image whose focal length is unknown.
Our method is the first that combines the following fea- tures: solving for both the 3D deformation and the cam- eras focal length, involving only local analytical solutions (there is no numerical optimization), being robust to mis- matches, handling general surfaces and running extremely fast. This was achieved through two key steps. First, an un- calibrated 3D deformation is computed thanks to a novel piecewise weak-perspective projection model. Second, the cameras focal length is estimated and enables upgrading the 3D deformation to metric. We use a variational frame- work, implemented using a smooth function basis and sam- pled local deformation models. The only degeneracy which we easily detect for focal length estimation is a flat and fronto-parallel surface.
Experimental results on simulated and real datasets show that our method achieves a 3D shape accuracy slightly below state of the art methods using a precalibrated or the true focal length, and a focal length accuracy slightly below static calibration methods.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: How do you tell a blackbird from a crow? There has been great progress toward automatic methods for visual recog- nition, including fine-grained visual categorization in which the classes to be distinguished are very similar. In a task such as bird species recognition, automatic recognition sys- tems can now exceed the performance of non-experts most people are challenged to name a couple dozen bird species, let alone identify them. This leads us to the question, Can a recognition system show humans what to look for when identifying classes (in this case birds)? In the context of fine-grained visual categorization, we show that we can au- tomatically determine which classes are most visually sim- ilar, discover what visual features distinguish very similar classes, and illustrate the key features in a way meaning- ful to humans. Running these methods on a dataset of bird images, we can generate a visual field guide to birds which includes a tree of similarity that displays the similarity re- lations between all species, pages for each species showing the most similar other species, and pages for each pair of similar species illustrating their differences.
|
Similar papers:
[rank all papers by similarity to this]
|
|
PhotoOCR: Reading Text in Uncontrolled Conditions [pdf]
Alessandro Bissacco, Mark Cummins, Yuval Netzer, Hartmut Neven |
|
Abstract: We describe PhotoOCR, a system for text extraction from images. Our particular focus is reliable text extraction from smartphone imagery, with the goal of text recognition as a user input modality similar to speech recognition. Commer- cially available OCR performs poorly on this task. Recent progress in machine learning has substantially improved isolated character classification; we build on this progress by demonstrating a complete OCR system using these tech- niques. We also incorporate modern datacenter-scale dis- tributed language modelling. Our approach is capable of recognizing text in a variety of challenging imaging con- ditions where traditional OCR systems fail, notably in the presence of substantial blur, low resolution, low contrast, high image noise and other distortions. It also operates with low latency; mean processing time is 600 ms per im- age. We evaluate our system on public benchmark datasets for text extraction and outperform all previously reported results, more than halving the error rate on multiple bench- marks. The system is currently in use in many applica- tions at Google, and is available as a user input modality in Google Translate for Android.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Finding Actors and Actions in Movies [pdf]
P. Bojanowski, F. Bach, I. Laptev, J. Ponce, C. Schmid, J. Sivic |
|
Abstract: We address the problem of learning a joint model of actors and actions in movies using weak supervision pro- vided by scripts. Specifically, we extract actor/action pairs from the script and use them as constraints in a discrimi- native clustering framework. The corresponding optimiza- tion problem is formulated as a quadratic program under linear constraints. People in video are represented by au- tomatically extracted and tracked faces together with cor- responding motion features. First, we apply the proposed framework to the task of learning names of characters in the movie and demonstrate significant improvements over previous methods used for this task. Second, we explore the joint actor/action constraint and show its advantage for weakly supervised action learning. We validate our method in the challenging setting of localizing and recog- nizing characters and their actions in feature length movies Casablanca and American Beauty.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Analysis of Scores, Datasets, and Models in Visual Saliency Prediction [pdf]
Ali Borji, Hamed R. Tavakoli, Dicky N. Sihite, Laurent Itti |
|
Abstract: Significant recent progress has been made in developing high-quality saliency models. However, less effort has been undertaken on fair assessment of these models, over large standardized datasets and correctly addressing confound- ing factors. In this study, we pursue a critical and quanti- tative look at challenges (e.g., center-bias, map smoothing) in saliency modeling and the way they affect model accu- racy. We quantitatively compare 32 state-of-the-art mod- els (using the shuffled AUC score to discount center-bias) on 4 benchmark eye movement datasets, for prediction of human fixation locations and scanpath sequence. We also account for the role of map smoothing. We find that, al- though model rankings vary, some (e.g., AWS, LG, AIM, and HouNIPS) consistently outperform other models over all datasets. Some models work well for prediction of both fix- ation locations and scanpath sequence (e.g., Judd, GBVS). Our results show low prediction accuracy for models over emotional stimuli from the NUSEF dataset. Our last bench- mark, for the first time, gauges the ability of models to de- code the stimulus category from statistics of fixations, sac- cades, and model saliency values at fixated locations. In this test, ITTI and AIM models win over other models. Our benchmark provides a comprehensive high-level picture of the strengths and weaknesses of many popular models, and suggests future research directions in saliency modeling.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Event Recognition in Photo Collections with a Stopwatch HMM [pdf]
Lukas Bossard, Matthieu Guillaumin, Luc Van_Gool |
|
Abstract: The task of recognizing events in photo collections is central for automatically organizing images. It is also very challenging, because of the ambiguity of photos across dif- ferent event classes and because many photos do not con- vey enough relevant information. Unfortunately, the field still lacks standard evaluation data sets to allow compar- ison of different approaches. In this paper, we introduce and release a novel data set of personal photo collections containing more than 61,000 images in 807 collections, an- notated with 14 diverse social event classes.
Casting collections as sequential data, we build upon re- cent and state-of-the-art work in event recognition in videos to propose a latent sub-event approach for event recogni- tion in photo collections. However, photos in collections are sparsely sampled over time and come in bursts from which transpires the importance of specific moments for the pho- tographers. Thus, we adapt a discriminative hidden Markov model to allow the transitions between states to be a func- tion of the time gap between consecutive images, which we coin as Stopwatch Hidden Markov model (SHMM).
In our experiments, we show that our proposed model outperforms approaches based only on feature pooling or a classical hidden Markov model. With an average accuracy of 56%, we also highlight the difficulty of the data set and the need for future advances in event recognition in photo collections.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Estimating the Material Properties of Fabric from Video [pdf]
Katherine L. Bouman, Bei Xiao, Peter Battaglia, William T. Freeman |
|
Abstract: Passively estimating the intrinsic material properties of deformable objects moving in a natural environment is es- sential for scene understanding. We present a framework to automatically analyze videos of fabrics moving under var- ious unknown wind forces, and recover two key material properties of the fabric: stiffness and area weight. We ex- tend features previously developed to compactly represent static image textures to describe video textures, such as fab- ric motion. A discriminatively trained regression model is then used to predict the physical properties of fabric from these features. The success of our model is demonstrated on a new, publicly available database of fabric videos with cor- responding measured ground truth material properties. We show that our predictions are well correlated with ground truth measurements of stiffness and density for the fabrics. Our contributions include: (a) a database that can be used for training and testing algorithms for passively predicting fabric properties from video, (b) an algorithm for predict- ing the material properties of fabric from a video, and (c) a perceptual study of humans ability to estimate the material properties of fabric from videos and images.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: Correspondence matching is one of the most common problems in computer vision, and it is often solved using photo-consistency of local regions. These approaches typ- ically assume that the frequency content in the local re- gion is consistent in the image pair, such that matching is performed on similar signals. However, in many practical situations this is not the case, for example with low depth of field cameras a scene point may be out of focus in one view and in-focus in the other, causing a mismatch of fre- quency signals. Furthermore, this mismatch can vary spa- tially over the entire image. In this paper we propose a local signal equalization approach for correspondence matching. Using a measure of local image frequency, we equalize lo- cal signals using an efficient scale-space image representa- tion such that their frequency contents are optimally suited for matching. Our approach allows better correspondence matching, which we demonstrate with a number of stereo reconstruction examples on synthetic and real datasets.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Bayesian 3D Tracking from Monocular Video [pdf]
Ernesto Brau, Jinyan Guan, Kyle Simek, Luca Del Pero, Colin Reimer Dawson, Kobus Barnard |
|
Abstract: We develop a Bayesian modeling approach for tracking people in 3D from monocular video with unknown cam- eras. Modeling in 3D provides natural explanations for occlusions and smoothness discontinuities that result from projection, and allows priors on velocity and smoothness to be grounded in physical quantities: meters and seconds vs. pixels and frames. We pose the problem in the context of data association, in which observations are assigned to tracks. A correct application of Bayesian inference to multi- target tracking must address the fact that the models di- mension changes as tracks are added or removed, and thus, posterior densities of different hypotheses are not compa- rable. We address this by marginalizing out the trajectory parameters so the resulting posterior over data associa- tions has constant dimension. This is made tractable by using (a) Gaussian process priors for smooth trajectories and (b) approximately Gaussian likelihood functions. Our approach provides a principled method for incorporating multiple sources of evidence; we present results using both optical flow and object detector outputs. Results are com- parable to recent work on 3D tracking and, unlike others, our method requires no pre-calibrated cameras.
|
Similar papers:
[rank all papers by similarity to this]
|
|
A General Dense Image Matching Framework Combining Direct and Feature-Based Costs [pdf]
Jim Braux-Zin, Romain Dupont, Adrien Bartoli |
|
Abstract: Dense motion field estimation (typically optical flow, stereo disparity and surface registration) is a key computer vision problem. Many solutions have been proposed to com- pute small or large displacements, narrow or wide baseline stereo disparity, but a unified methodology is still lacking. We here introduce a general framework that robustly com- bines direct and feature-based matching. The feature-based cost is built around a novel robust distance function that handles keypoints and weak features such as segments. It allows us to use putative feature matches which may con- tain mismatches to guide dense motion estimation out of local minima. Our framework uses a robust direct data term (AD-Census). It is implemented with a powerful second or- der Total Generalized Variation regularization with external and self-occlusion reasoning. Our framework achieves state of the art performance in several cases (standard optical flow benchmarks, wide-baseline stereo and non-rigid sur- face registration). Our framework has a modular design that customizes to specific application needs.
Introduction
A dense motion field, also called optical flow, is a very useful cue for problems such as tracking, segmentation, local- ization and reconstruction, or non-rigid surfaces registration. Optical flow estimation is an old computer vision problem. While early techniques were patch-based [19], current ones estimate dense flow fields with variational methods built upon the work by Horn and Schu
|
Similar papers:
[rank all papers by similarity to this]
|
|
Robust Face Landmark Estimation under Occlusion [pdf]
Xavier P. Burgos-Artizzu, Pietro Perona, Piotr Dollar |
|
Abstract: Human faces captured in real-world conditions present large variations in shape and occlusions due to differences in pose, expression, use of accessories such as sunglasses and hats and interactions with objects (e.g. food). Current face landmark estimation approaches struggle under such conditions since they fail to provide a principled way of han- dling outliers. We propose a novel method, called Robust Cascaded Pose Regression (RCPR) which reduces exposure to outliers by detecting occlusions explicitly and using ro- bust shape-indexed features. We show that RCPR improves on previous landmark estimation methods on three popu- lar face datasets (LFPW, LFW and HELEN). We further explore RCPRs performance by introducing a novel face dataset focused on occlusion, composed of 1,007 faces pre- senting a wide range of occlusion patterns. RCPR reduces failure cases by half on all four datasets, at the same time as it detects face occlusions with a 80/40% precision/recall.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: In this paper, we propose a new family of binary local feature descriptors called nested shape descriptors. These descriptors are constructed by pooling oriented gradients over a large geometric structure called the Hawaiian ear- ring, which is constructed with a nested correlation struc- ture that enables a new robust local distance function called the nesting distance. This distance function is unique to the nested descriptor and provides robustness to outliers from order statistics. In this paper, we define the nested shape descriptor family and introduce a specific member called the seed-of-life descriptor. We perform a trade study to de- termine optimal descriptor parameters for the task of im- age matching. Finally, we evaluate performance compared to state-of-the-art local feature descriptors on the VGG- Affine image matching benchmark, showing significant per- formance gains. Our descriptor is the first binary descriptor to outperform SIFT on this benchmark.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Unifying Nuclear Norm and Bilinear Factorization Approaches for Low-Rank Matrix Decomposition [pdf]
Ricardo Cabral, Fernando De_La_Torre, Joao P. Costeira, Alexandre Bernardino |
|
Abstract: Bilinear factorization:
minf(XUV) U,V
Nuclear norm regularization:
minf(XZ)+Z Z
Variational definition of the nuclear norm:
Z= min 1U2+V2 Z=UV 2 F F
Unified model:
minf(XUV)+U2 +V2 U,V 2FF
Low rank models have been widely used for the represen- tation of shape, appearance or motion in computer vision problems. Traditional approaches to fit low rank models make use of an explicit bilinear factorization. These ap- proaches benefit from fast numerical methods for optimiza- tion and easy kernelization. However, they suffer from seri- ous local minima problems depending on the loss function and the amount/type of missing data. Recently, these low- rank models have alternatively been formulated as convex problems using the nuclear norm regularizer; unlike factor- ization methods, their numerical solvers are slow and it is unclear how to kernelize them or to impose a rank a priori.
This paper proposes a unified approach to bilinear fac- torization and nuclear norm regularization, that inherits the benefits of both. We analyze the conditions under which these approaches are equivalent. Moreover, based on this analysis, we propose a new optimization algorithm and a rank continuation strategy that outperform state-of-the- art approaches for Robust PCA, Structure from Motion and Photometric Stereo with outliers and missing data.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Heterogeneous Image Features Integration via Multi-modal Semi-supervised Learning Model [pdf]
Xiao Cai, Feiping Nie, Weidong Cai, Heng Huang |
|
Abstract: Automatic image categorization has become increas- ingly important with the development of Internet and the growth in the size of image databases. Although the im- age categorization can be formulated as a typical multi- class classification problem, two major challenges have been raised by the real-world images. On one hand, though using more labeled training data may improve the predic- tion performance, obtaining the image labels is a time con- suming as well as biased process. On the other hand, more and more visual descriptors have been proposed to describe objects and scenes appearing in images and different fea- tures describe different aspects of the visual characteristics. Therefore, how to integrate heterogeneous visual features to do the semi-supervised learning is crucial for categorizing large-scale image data. In this paper, we propose a novel approach to integrate heterogeneous features by performing multi-modal semi-supervised classification on unlabeled as well as unsegmented images. Considering each type of fea- ture as one modality, taking advantage of the large amoun- t of unlabeled data information, our new adaptive multi- modal semi-supervised classification (AMMSS) algorithm learns a commonly shared class indicator matrix and the weights for different modalities (image features) simultane- ously.
|
Similar papers:
[rank all papers by similarity to this]
|
|
New Graph Structured Sparsity Model for Multi-label Image Annotations [pdf]
Xiao Cai, Feiping Nie, Weidong Cai, Heng Huang |
|
Abstract: In multi-label image annotations, because each image is associated to multiple categories, the semantic terms (la- bel classes) are not mutually exclusive. Previous research showed that such label correlations can largely boost the annotation accuracy. However, all existing methods only directly apply the label correlation matrix to enhance the label inference and assignment without further learning the structural information among classes. In this paper, we model the label correlations using the relational graph, and propose a novel graph structured sparse learning model to incorporate the topological constraints of relation graph in multi-label classifications. As a result, our new method will capture and utilize the hidden class structures in relational graph to improve the annotation results. In proposed objec- tive, a large number of structured sparsity-inducing norm- s are utilized, thus the optimization becomes difficult. To solve this problem, we derive an efficient optimization algo- rithm with proved convergence. We perform extensive ex- periments on six multi-label image annotation benchmark data sets. In all empirical results, our new method shows better annotation results than the state-of-the-art approach- es.
|
Similar papers:
[rank all papers by similarity to this]
|
|
An Enhanced Structure-from-Motion Paradigm Based on the Absolute Dual Quadric and Images of Circular Points [pdf]
Lilian Calvet, Pierre Gurdjos |
|
Abstract: This work aims at introducing a new unified Structure- from-Motion (SfM) paradigm in which images of circular point-pairs can be combined with images of natural points. An imaged circular point-pair encodes the 2D Euclidean structure of a world plane and can easily be derived from the image of a planar shape, especially those including cir- cles. A classical SfM method generally runs two steps: first a projective factorization of all matched image points (into projective cameras and points) and second a camera self- calibration that updates the obtained world from projective to Euclidean. This work shows how to introduce images of circular points in these two SfM steps while its key contri- bution is to provide the theoretical foundations for combin- ing classical linear self-calibration constraints with ad- ditional ones derived from such images. We show that the two proposed SfM steps clearly contribute to better results than the classical approach. We validate our contributions on synthetic and real images.
|
Similar papers:
[rank all papers by similarity to this]
|
|
A Practical Transfer Learning Algorithm for Face Verification [pdf]
Xudong Cao, David Wipf, Fang Wen, Genquan Duan, Jian Sun |
|
Abstract: Face verification involves determining whether a pair of facial images belongs to the same or different subjects. This problem can prove to be quite challenging in many im- portant applications where labeled training data is scarce, e.g., family album photo organization software. Herein we propose a principled transfer learning approach for merg- ing plentiful source-domain data with limited samples from some target domain of interest to create a classifier that ide- ally performs nearly as well as if rich target-domain data were present. Based upon a surprisingly simple generative Bayesian model, our approach combines a KL-divergence- based regularizer/prior with a robust likelihood function leading to a scalable implementation via the EM algorithm. As justification for our design choices, we later use prin- ciples from convex analysis to recast our algorithm as an equivalent structured rank minimization problem leading to a number of interesting insights related to solution struc- ture and feature-transform invariance. These insights help to both explain the effectiveness of our algorithm as well as elucidate a wide variety of related Bayesian approaches. Experimental testing with challenging datasets validate the utility of the proposed algorithm.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: Recently, there is a considerable amount of efforts de- voted to the problem of unconstrained face verification, where the task is to predict whether pairs of images are from the same person or not. This problem is challenging and difficult due to the large variations in face images. In this paper, we develop a novel regularization framework to learn similarity metrics for unconstrained face verification. We formulate its objective function by incorporating the ro- bustness to the large intra-personal variations and the dis- criminative power of novel similarity metrics. In addition, our formulation is a convex optimization problem which guarantees the existence of its global solution. Experiments show that our proposed method achieves the state-of-the-art results on the challenging Labeled Faces in the Wild (LFW) database [10].
|
Similar papers:
[rank all papers by similarity to this]
|
|
SYM-FISH: A Symmetry-Aware Flip Invariant Sketch Histogram Shape Descriptor [pdf]
Xiaochun Cao, Hua Zhang, Si Liu, Xiaojie Guo, Liang Lin |
|
Abstract: Recently, studies on sketch, such as sketch retrieval and sketch classification, have received more attention in the computer vision community. One of its most fundamental and essential problems is how to more effectively describe a sketch image. Many existing descriptors, such as shape context, have achieved great success. In this paper, we pro- pose a new descriptor, namely Symmetric-aware Flip In- variant Sketch Histogram (SYM-FISH) to refine the shape context feature. Its extraction process includes three steps. First the Flip Invariant Sketch Histogram (FISH) descrip- tor is extracted on the input image, which is a flip-invariant version of the shape context feature. Then we explore the symmetry character of the image by calculating the kurto- sis coefficient. Finally, the SYM-FISH is generated by con- structing a symmetry table. The new SYM-FISH descrip- tor supplements the original shape context by encoding the symmetric information, which is a pervasive characteristic of natural scene and objects. We evaluate the efficacy of the novel descriptor in two applications, i.e., sketch retrieval and sketch classification. Extensive experiments on three datasets well demonstrate the effectiveness and robustness of the proposed SYM-FISH descriptor.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Symbiotic Segmentation and Part Localization for Fine-Grained Categorization [pdf]
Yuning Chai, Victor Lempitsky, Andrew Zisserman |
|
Abstract: We propose a new method for the task of fine-grained vi- sual categorization. The method builds a model of the base- level category that can be fitted to images, producing high- quality foreground segmentation and mid-level part local- izations. The model can be learnt from the typical datasets available for fine-grained categorization, where the only annotation provided is a loose bounding box around the in- stance (e.g. bird) in each image. Both segmentation and part localizations are then used to encode the image con- tent into a highly-discriminative visual signature.
The model is symbiotic in that part discov- ery/localization is helped by segmentation and, conversely, the segmentation is helped by the detection (e.g. part layout). Our model builds on top of the part-based object category detector of Felzenszwalb et al., and also on the powerful GrabCut segmentation algorithm of Rother et al., and adds a simple spatial saliency coupling between them. In our evaluation, the model improves the categorization accuracy over the state-of-the-art. It also improves over what can be achieved with an analogous system that runs segmentation and part-localization independently.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Rectangling Stereographic Projection for Wide-Angle Image Visualization [pdf]
Che-Han Chang, Min-Chun Hu, Wen-Huang Cheng, Yung-Yu Chuang |
|
Abstract: This paper proposes a new projection model for map- ping a hemisphere to a plane. Such a model can be use- ful for viewing wide-angle images. Our model consists of two steps. In the first step, the hemisphere is projected onto a swung surface constructed by a circular profile and a rounded rectangular trajectory. The second step maps the projected image on the swung surface onto the image plane through the perspective projection. We also propose a method for automatically determining proper parameters for the projection model based on image content. The pro- posed model has several advantages. It is simple, efficient and easy to control. Most importantly, it makes a bet- ter compromise between distortion minimization and line preserving than popular projection models, such as stereo- graphic and Pannini projections. Experiments and analysis demonstrate the effectiveness of our model.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Stacked Predictive Sparse Coding for Classification of Distinct Regions in Tumor Histopathology [pdf]
Hang Chang, Yin Zhou, Paul Spellman, Bahram Parvin |
|
Abstract: Image-based classification of histology sections, in terms of distinct components (e.g., tumor, stroma, normal), pro- vides a series of indices for tumor composition. Further- more, aggregation of these indices, from each whole slide image (WSI) in a large cohort, can provide predictive mod- els of the clinical outcome. However, performance of the existing techniques is hindered as a result of large technical variations and biological heterogeneities that are always present in a large cohort. We propose a system that au- tomatically learns a series of basis functions for represent- ing the underlying spatial distribution using stacked pre- dictive sparse decomposition (PSD). The learned represen- tation is then fed into the spatial pyramid matching frame- work (SPM) with a linear SVM classifier. The system has been evaluated for classification of (a) distinct histological components for two cohorts of tumor types, and (b) colony organization of normal and malignant cell lines in 3D cell culture models. Throughput has been increased through the utility of graphical processing unit (GPU), and evalu- ation indicates a superior performance results, compared with previous research.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: We present an integrated probabilistic model for layered object tracking that combines dynamics on implicit shape representations, topological shape constraints, adaptive ap- pearance models, and layered flow. The generative model combines the evolution of appearances and layer shapes with a Gaussian process flow and explicit layer ordering. Efficient MCMC sampling algorithms are developed to en- able a particle filtering approach while reasoning about the distribution of object boundaries in video. We demonstrate the utility of the proposed tracking algorithm on a wide vari- ety of video sources while achieving state-of-the-art results on a boundary-accurate tracking dataset.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: In this paper we address the problem of robust and effi- cient averaging of relative 3D rotations. Apart from having an interesting geometric structure, robust rotation averag- ing addresses the need for a good initialization for large- scale optimization used in structure-from-motion pipelines. Such pipelines often use unstructured image datasets har- vested from the internet thereby requiring an initialization method that is robust to outliers. Our approach works on the Lie group structure of 3D rotations and solves the prob- lem of large-scale robust rotation averaging in two ways. Firstly, we use modern l1 optimizers to carry out robust av- eraging of relative rotations that is efficient, scalable and robust to outliers. In addition, we also develop a two- step method that uses the l1 solution as an initialisation for an iteratively reweighted least squares (IRLS) approach. These methods achieve excellent results on large-scale, real world datasets and significantly outperform existing meth- ods, i.e. the state-of-the-art discrete-continuous optimiza- tion method of [3] as well as the Weiszfeld method of [8]. We demonstrate the efficacy of our method on two large- scale real world datasets and also provide the results of the two aforementioned methods for comparison.
|
Similar papers:
[rank all papers by similarity to this]
|
|
A Generalized Low-Rank Appearance Model for Spatio-temporally Correlated Rain Streaks [pdf]
Yi-Lei Chen, Chiou-Ting Hsu |
|
Abstract: In this paper, we propose a novel low-rank appearance model for removing rain streaks. Different from previous work, our method needs neither rain pixel detection nor time-consuming dictionary learning stage. Instead, as rain streaks usually reveal similar and repeated patterns on imaging scene, we propose and generalize a low-rank model from matrix to tensor structure in order to capture the spatio-temporally correlated rain streaks. With the appearance model, we thus remove rain streaks from image/video (and also other high-order image structure) in a unified way. Our experimental results demonstrate competitive (or even better) visual quality and efficient run-time in comparison with state of the art.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: We present a model for intrinsic decomposition of RGB-D images. Our approach analyzes a single RGB-D image and estimates albedo and shading fields that explain the input. To disambiguate the problem, our model esti- mates a number of components that jointly account for the reconstructed shading. By decomposing the shading field, we can build in assumptions about image formation that help distinguish reflectance variation from shading. These assumptions are expressed as simple nonlocal regularizers. We evaluate the model on real-world images and on a chal- lenging synthetic dataset. The experimental results demon- strate that the presented approach outperforms prior mod- els for intrinsic decomposition of RGB-D images.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Accurate and Robust 3D Facial Capture Using a Single RGBD Camera [pdf]
Yen-Lin Chen, Hsiang-Tao Wu, Fuhao Shi, Xin Tong, Jinxiang Chai |
|
Abstract: This paper presents an automatic and robust approach that accurately captures high-quality 3D facial perfor- mances using a single RGBD camera. The key of our ap- proach is to combine the power of automatic facial feature detection and image-based 3D nonrigid registration tech- niques for 3D facial reconstruction. In particular, we de- velop a robust and accurate image-based nonrigid regis- tration algorithm that incrementally deforms a 3D template mesh model to best match observed depth image data and important facial features detected from single RGBD im- ages. The whole process is fully automatic and robust be- cause it is based on single frame facial registration frame- work. The system is flexible because it does not require any strong 3D facial priors such as blendshape models. We demonstrate the power of our approach by capturing a wide range of 3D facial expressions using a single RGBD camera and achieve state-of-the-art accuracy by comparing against alternative methods.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Constructing Adaptive Complex Cells for Robust Visual Tracking [pdf]
Dapeng Chen, Zejian Yuan, Yang Wu, Geng Zhang, Nanning Zheng |
|
Abstract: Representation is a fundamental problem in object track- ing. Conventional methods track the target by describing its local or global appearance. In this paper we present that, besides the two paradigms, the composition of local region histograms can also provide diverse and important object cues. We use cells to extract local appearance, and construct complex cells to integrate the information from cells. With different spatial arrangements of cells, complex cells can explore various contextual information at multiple scales, which is important to improve the tracking perfor- mance. We also develop a novel template-matching algo- rithm for object tracking, where the template is composed of temporal varying cells and has two layers to capture the target and background appearance respectively. An adap- tive weight is associated with each complex cell to cope with occlusion as well as appearance variation. A fusion weight is associated with each complex cell type to preserve the global distinctiveness. Our algorithm is evaluated on 25 challenging sequences, and the results not only confirm the contribution of each component in our tracking system, but also outperform other competing trackers.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Group Norm for Learning Structured SVMs with Unstructured Latent Variables [pdf]
Daozheng Chen, Dhruv Batra, William T. Freeman |
|
Abstract: Latent variables models have been applied to a number of computer vision problems. However, the complexity of the latent space is typically left as a free design choice. A larger latent space results in a more expressive model, but such models are prone to overfitting and are slower to per- form inference with. The goal of this paper is to regularize the complexity of the latent space and learn which hidden states are really relevant for prediction. Specifically, we propose using group-sparsity-inducing regularizers such as l1-l2 to estimate the parameters of Structured SVMs with unstructured latent variables. Our experiments on digit recognition and object detection show that our approach is indeed able to control the complexity of latent space without any significant loss in accuracy of the learnt model.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Learning a Dictionary of Shape Epitomes with Applications to Image Labeling [pdf]
Liang-Chieh Chen, George Papandreou, Alan L. Yuille |
|
Abstract: The first main contribution of this paper is a novel method for representing images based on a dictionary of shape epitomes. These shape epitomes represent the local edge structure of the image and include hidden variables to encode shift and rotations. They are learnt in an unsu- pervised manner from groundtruth edges. This dictionary is compact but is also able to capture the typical shapes of edges in natural images. In this paper, we illustrate the shape epitomes by applying them to the image labeling task. In other work, described in the supplementary material, we apply them to edge detection and image modeling.
We apply shape epitomes to image labeling by using Conditional Random Field (CRF) Models. They are alter- natives to the superpixel or pixel representations used in most CRFs. In our approach, the shape of an image patch is encoded by a shape epitome from the dictionary. Unlike the superpixel representation, our method avoids making early decisions which cannot be reversed. Our resulting hi- erarchical CRFs efficiently capture both local and global class co-occurrence properties. We demonstrate its quanti- tative and qualitative properties of our approach with image labeling experiments on two standard datasets: MSRC-21 and Stanford Background.
|
Similar papers:
[rank all papers by similarity to this]
|
|
NEIL: Extracting Visual Knowledge from Web Data [pdf]
Xinlei Chen, Abhinav Shrivastava, Abhinav Gupta |
|
Abstract: We propose NEIL (Never Ending Image Learner), a com- puter program that runs 24 hours per day and 7 days per week to automatically extract visual knowledge from In- ternet data. NEIL uses a semi-supervised learning algo- rithm that jointly discovers common sense relationships (e.g., Corolla is a kind of/looks similar to Car,Wheel is a part of Car) and labels instances of the given visual categories. It is an attempt to develop the worlds largest visual structured knowledge base with minimum human la- beling effort. As of 10th October 2013, NEIL has been con- tinuously running for 2.5 months on 200 core cluster (more than 350K CPU hours) and has an ontology of 1152 object categories, 1034 scene categories and 87 attributes. During this period, NEIL has discovered more than 1700 relation- ships and has labeled more than 400K visual instances.
1. Motivation
Recent successes in computer vision can be primarily at- tributed to the ever increasing size of visual knowledge in terms of labeled instances of scenes, objects, actions, at- tributes, and the contextual relationships between them. But as we move forward, a key question arises: how will we gather this structured visual knowledge on a vast scale? Re- cent efforts such as ImageNet [8] and Visipedia [30] have tried to harness human intelligence for this task. However, we believe that these approaches lack both the richness and the scalability required for gathering massive amounts of visual knowledge. For example, at the
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: Sparsity models have recently shown great promise in many vision tasks. Using a learned dictionary in sparsity models can in general outperform predefined bases in clean data. In practice, both training and testing data may be corrupted and contain noises and outliers. Although recent studies attempted to cope with corrupted data and achieved encouraging results in testing phase, how to handle corrup- tion in training phase still remains a very difficult problem. In contrast to most existing methods that learn the dictio- nary from clean data, this paper is targeted at handling cor- ruptions and outliers in training data for dictionary learn- ing. We propose a general method to decompose the recon- structive residual into two components: a non-sparse com- ponent for small universal noises and a sparse component for large outliers, respectively. In addition, further analysis reveals the connection between our approach and the par- tial dictionary learning approach, updating only part of the prototypes (or informative codewords) with remaining (or noisy codewords) fixed. Experiments on synthetic data as well as real applications have shown satisfactory per- formance of this new robust dictionary learning approach.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Efficient Salient Region Detection with Soft Image Abstraction [pdf]
Ming-Ming Cheng, Jonathan Warrell, Wen-Yan Lin, Shuai Zheng, Vibhav Vineet, Nigel Crook |
|
Abstract: on
Ming-Ming Cheng Jonathan Warrell Wen-Yan Lin Shuai Zheng Vision Group, Oxford Brookes University
Vibhav Vineet
(b) Our result
Nigel Crook
(c) Ground truth
Abstract
Detecting visually salient regions in images is one of the fundamental problems in computer vision. We propose a novel method to decompose an image into large scale per- ceptually homogeneous elements for efficient salient region detection, using a soft image abstraction representation. By considering both appearance similarity and spatial dis- tribution of image pixels, the proposed representation ab- stracts out unnecessary image details, allowing the assign- ment of comparable saliency values across similar regions, and producing perceptually accurate salient region detec- tion. We evaluate our salient region detection approach on the largest publicly available dataset with pixel accurate annotations. The experimental results show that the pro- posed method outperforms 18 alternate methods, reducing the mean absolute error by 25.2% compared to the previous best result, while being computationally more efficient.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Rank Minimization across Appearance and Shape for AAM Ensemble Fitting [pdf]
Xin Cheng, Sridha Sridharan, Jason Saragih, Simon Lucey |
|
Abstract: Active Appearance Models (AAMs) employ a paradigm of inverting a synthesis model of how an object can vary in terms of shape and appearance. As a result, the abil- ity of AAMs to register an unseen object image is intrin- sically linked to two factors. First, how well the synthesis model can reconstruct the object image. Second, the de- grees of freedom in the model. Fewer degrees of freedom yield a higher likelihood of good fitting performance. In this paper we look at how these seemingly contrasting factors can complement one another for the problem of AAM fitting of an ensemble of images stemming from a constrained set (e.g. an ensemble of face images of the same person).
|
Similar papers:
[rank all papers by similarity to this]
|
|
Affine-Constrained Group Sparse Coding and Its Application to Image-Based Classifications [pdf]
Yu-Tseh Chi, Mohsen Ali, Muhammad Rushdi, Jeffrey Ho |
|
Abstract: This paper proposes a novel approach for sparse coding that further improves upon the sparse representation-based classification (SRC) framework. The proposed framework, Affine-Constrained Group Sparse Coding (ACGSC), ex- tends the current SRC framework to classification problems with multiple input samples. Geometrically, the affine- constrained group sparse coding essentially searches for the vector in the convex hull spanned by the input vectors that can best be sparse coded using the given dictionary. The resulting objective function is still convex and can be ef- ficiently optimized using iterative block-coordinate descent scheme that is guaranteed to converge. Furthermore, we provide a form of sparse recovery result that guarantees, at least theoretically, that the classification performance of the constrained group sparse coding should be at least as good as the group sparse coding. We have evaluated the proposed approach using three different recognition ex- periments that involve illumination variation of faces and textures, and face recognition under occlusions. Prelimi- nary experiments have demonstrated the effectiveness of the proposed approach, and in particular, the results from the recognition/occlusion experiment are surprisingly accurate and robust.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Multi-attributed Dictionary Learning for Sparse Coding [pdf]
Chen-Kuo Chiang, Te-Feng Su, Chih Yen, Shang-Hong Lai |
|
Abstract: We present a multi-attributed dictionary learning algo- rithm for sparse coding. Considering training samples with multiple attributes, a new distance matrix is proposed by jointly incorporating data and attribute similarities. Then, an objective function is presented to learn category- dependent dictionaries that are compact (closeness of dic- tionary atoms based on data distance and attribute similar- ity), reconstructive (low reconstruction error with correct dictionary) and label-consistent (encouraging the labels of dictionary atoms to be similar). We have demonstrated our algorithm on action classification and face recognition tasks on several publicly available datasets. Experimental results with improved performance over previous dictionary learning methods are shown to validate the effectiveness of the proposed algorithm.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: Many tasks in computer vision are formulated as graph matching problems. Despite the NP-hard nature of the problem, fast and accurate approximations have led to sig- nificant progress in a wide range of applications. Learning graph models from observed data, however, still remains a challenging issue. This paper presents an effective scheme to parameterize a graph model, and learn its structural at- tributes for visual object matching. For this, we propose a graph representation with histogram-based attributes, and optimize them to increase the matching accuracy. Exper- imental evaluations on synthetic and real image datasets demonstrate the effectiveness of our approach, and show significant improvement in matching accuracy over graphs with pre-defined structures.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Modeling the Calibration Pipeline of the Lytro Camera for High Quality Light-Field Image Reconstruction [pdf]
Donghyeon Cho, Minhaeng Lee, Sunyeong Kim, Yu-Wing Tai |
|
Abstract: Light-field imaging systems have got much attention re- cently as the next generation camera model. A light-field imaging system consists of three parts: data acquisition, manipulation, and application. Given an acquisition sys- tem, it is important to understand how a light-field camera converts from its raw image to its resulting refocused image. In this paper, using the Lytro camera as an example, we de- scribe step-by-step procedures to calibrate a raw light-field image. In particular, we are interested in knowing the spa- tial and angular coordinates of the micro lens array and the resampling process for image reconstruction. Since Lytro uses a hexagonal arrangement of a micro lens image, ad- ditional treatments in calibration are required. After cali- bration, we analyze and compare the performances of sev- eral resampling methods for image reconstruction with and without calibration. Finally, a learning based interpolation method is proposed which demonstrates a higher quality image reconstruction than previous interpolation methods including a method used in Lytro software.
|
Similar papers:
[rank all papers by similarity to this]
|
|
A Learning-Based Approach to Reduce JPEG Artifacts in Image Matting [pdf]
Inchang Choi, Sunyeong Kim, Michael S. Brown, Yu-Wing Tai |
|
Abstract: Single image matting techniques assume high-quality in- put images. The vast majority of images on the web and in personal photo collections are encoded using JPEG com- pression. JPEG images exhibit quantization artifacts that adversely affect the performance of matting algorithms.
To address this situation, we propose a learning-based post-processing method to improve the alpha mattes ex- tracted from JPEG images. Our approach learns a set of sparse dictionaries from training examples that are used to transfer details from high-quality alpha mattes to alpha mattes corrupted by JPEG compression. Three different dictionaries are defined to accommodate different object structure (long hair, short hair, and sharp boundaries). A back-projection criteria combined within an MRF frame- work is used to automatically select the best dictionary to apply on the objects local boundary. We demonstrate that our method can produces superior results over existing state-of-the-art matting algorithms on a variety of inputs and compression levels.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Segmentation Driven Object Detection with Fisher Vectors [pdf]
Ramazan Gokberk Cinbis, Jakob Verbeek, Cordelia Schmid |
|
Abstract: We present an object detection system based on the Fisher vector (FV) image representation computed over SIFT and color descriptors. For computational and stor- age efficiency, we use a recent segmentation-based method to generate class-independent object detection hypotheses, in combination with data compression techniques. Our main contribution is a method to produce tentative object segmentation masks to suppress background clutter in the features. Re-weighting the local image features based on these masks is shown to improve object detection signifi- cantly. We also exploit contextual features in the form of a full-image FV descriptor, and an inter-category rescoring mechanism. Our experiments on the PASCAL VOC 2007 and 2010 datasets show that our detector improves over the current state-of-the-art detection results.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Cosegmentation and Cosketch by Unsupervised Learning [pdf]
Jifeng Dai, Ying Nian Wu, Jie Zhou, Song-Chun Zhu |
|
Abstract: Cosegmentation refers to the problem of segmenting mul- tiple images simultaneously by exploiting the similarities between the foreground and background regions in these images. The key issue in cosegmentation is to align com- mon objects between these images. To address this issue, we propose an unsupervised learning framework for coseg- mentation, by coupling cosegmentation with what we call cosketch. The goal of cosketch is to automatically dis- cover a codebook of deformable shape templates shared by the input images. These shape templates capture distinct image patterns and each template is matched to similar im- age patches in different images. Thus the cosketch of the images helps to align foreground objects, thereby providing crucial information for cosegmentation. We present a sta- tistical model whose energy function couples cosketch and cosegmentation. We then present an unsupervised learn- ing algorithm that performs cosketch and cosegmentation by energy minimization. Experiments show that our method outperforms state of the art methods for cosegmentation on the challenging MSRC and iCoseg datasets. We also illustrate our method on a new dataset called Coseg-Rep where cosegmentation can be performed within a single im- age with repetitive patterns.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: This paper investigates the problem of semi-supervised classification. Unlike previous methods to regularize clas- sifying boundaries with unlabeled data, our method learns a new image representation from all available data (labeled and unlabeled) and performs plain supervised learning with the new feature. In particular, an ensemble of image pro- totype sets are sampled automatically from the available data, to represent a rich set of visual categories/attributes. Discriminative functions are then learned on these proto- type sets, and image are represented by the concatenation of their projected values onto the prototypes (similarities to them) for further classification. Experiments on four standard datasets show three interesting phenomena: (1) our method consistently outperforms previous methods for semi-supervised image classification; (2) our method lets it- self combine well with these methods; and (3) our method works well for self-taught image classification where unla- beled data are not coming from the same distribution as la- beled ones, but rather from a random collection of images.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Example-Based Facade Texture Synthesis [pdf]
Dengxin Dai, Hayko Riemenschneider, Gerhard Schmitt, Luc Van_Gool |
|
Abstract: There is an increased interest in the efficient creation of city models, be it virtual or as-built. We present a method for synthesizing complex, photo-realistic facade images, from a single example. After parsing the example image into its semantic components, a tiling for it is generated. Novel tilings can then be created, yielding facade textures with different dimensions or with occluded parts inpainted. A genetic algorithm guides the novel facades as well as inpainted parts to be consistent with the example, both in terms of their overall structure and their detailed textures. Promising results for multiple standard datasets in partic- ular for the different building styles they contain demon- strate the potential of the method.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: Photo-sequencing is the problem of recovering the tem- poral order of a set of still images of a dynamic event, taken asynchronously by a set of uncalibrated cameras. Solving this problem is a first, crucial step for analyzing (or vi- sualizing) the dynamic content of the scene captured by a large number of freely moving spectators. We propose a geometric based solution, followed by rank aggregation to the photo-sequencing problem. Our algorithm trades spa- tial certainty for temporal certainty. Whereas the previous solution proposed by [4] relies on two images taken from the same static camera to eliminate uncertainty in space, we drop the static-camera assumption and replace it with temporal information available from images taken from the same (moving) camera. Our method thus overcomes the limitation of the static-camera assumption, and scales much better with the duration of the event and the spread of cam- eras in space. We present successful results on challenging real data sets and large scale synthetic data (250 images).
|
Similar papers:
[rank all papers by similarity to this]
|
|
Visual Reranking through Weakly Supervised Multi-graph Learning [pdf]
Cheng Deng, Rongrong Ji, Wei Liu, Dacheng Tao, Xinbo Gao |
|
Abstract: Visual reranking has been widely deployed to refine the quality of conventional content-based image retrieval en- gines. The current trend lies in employing a crowd of re- trieved results stemming from multiple feature modalities to boost the overall performance of visual reranking. Howev- er, a major challenge pertaining to current reranking meth- ods is how to take full advantage of the complementary property of distinct feature modalities. Given a query im- age and one feature modality, a regular visual reranking framework treats the top-ranked images as pseudo positive instances which are inevitably noisy, difficult to reveal this complementary property, and thus lead to inferior ranking performance. This paper proposes a novel image rerank- ing approach by introducing a Co-Regularized Multi-Graph Learning (Co-RMGL) framework, in which the intra-graph and inter-graph constraints are simultaneously imposed to encode affinities in a single graph and consistency across d- ifferent graphs. Moreover, weakly supervised learning driv- en by image attributes is performed to denoise the pseudo- labeled instances, thereby highlighting the unique strength of individual feature modality. Meanwhile, such learning can yield a few anchors in graphs that vitally enable the alignment and fusion of multiple graphs. As a result, an edge weight matrix learned from the fused graph automat- ically gives the ordering to the initially retrieved results. We evaluate our approach on four benchmark
|
Similar papers:
[rank all papers by similarity to this]
|
|
Detecting Dynamic Objects with Multi-view Background Subtraction [pdf]
Raul Diaz, Sam Hallman, Charless C. Fowlkes |
|
Abstract: The confluence of robust algorithms for structure from motion along with high-coverage mapping and imaging of the world around us suggests that it will soon be feasible to accurately estimate camera pose for a large class pho- tographs taken in outdoor, urban environments. In this pa- per, we investigate how such information can be used to improve the detection of dynamic objects such as pedes- trians and cars. First, we show that when rough camera location is known, we can utilize detectors that have been trained with a scene-specific background model in order to improve detection accuracy. Second, when precise camera pose is available, dense matching to a database of exist- ing images using multi-view stereo provides a way to elim- inate static backgrounds such as building facades, akin to background-subtraction often used in video analysis. We evaluate these ideas using a dataset of tourist photos with estimated camera pose. For template-based pedestrian de- tection, we achieve a 50 percent boost in average precision over baseline.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Like Father, Like Son: Facial Expression Dynamics for Kinship Verification [pdf]
Hamdi Dibeklioglu, Albert Ali Salah, Theo Gevers |
|
Abstract: Kinship verification from facial appearance is a difficult problem. This paper explores the possibility of employing facial expression dynamics in this problem. By using fea- tures that describe facial dynamics and spatio-temporal ap- pearance over smile expressions, we show that it is possible to improve the state of the art in this problem, and verify that it is indeed possible to recognize kinship by resemblance of facial expressions. The proposed method is tested on differ- ent kin relationships. On the average, 72.89% verification accuracy is achieved on spontaneous smiles.
|
Similar papers:
[rank all papers by similarity to this]
|
|
The Way They Move: Tracking Multiple Targets with Similar Appearance [pdf]
Caglayan Dicle, Octavia I. Camps, Mario Sznaier |
|
Abstract: We introduce a computationally efficient algorithm for multi-object tracking by detection that addresses four main challenges: appearance similarity among targets, missing data due to targets being out of the field of view or oc- cluded behind other objects, crossing trajectories, and cam- era motion. The proposed method uses motion dynamics as a cue to distinguish targets with similar appearance, min- imize target mis-identification and recover missing data. Computational efficiency is achieved by using a General- ized Linear Assignment (GLA) coupled with efficient proce- dures to recover missing data and estimate the complexity of the underlying dynamics. The proposed approach works with tracklets of arbitrary length and does not assume a dynamical model a priori, yet it captures the overall mo- tion dynamics of the targets. Experiments using challenging videos show that this framework can handle complex target motions, non-stationary cameras and long occlusions, on scenarios where appearance cues are not available or poor.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Facial Action Unit Event Detection by Cascade of Tasks [pdf]
Xiaoyu Ding, Wen-Sheng Chu, Fernando De_La_Torre, Jeffery F. Cohn, Qiao Wang |
|
Abstract: Automatic facial Action Unit (AU) detection from video is a long-standing problem in facial expression analysis. AU detection is typically posed as a classification problem between frames or segments of positive examples and neg- ative ones, where existing work emphasizes the use of dif- ferent features or classifiers. In this paper, we propose a method called Cascade of Tasks (CoT) that combines the use of different tasks (i.e., frame, segment and transition) for AU event detection. We train CoT in a sequential manner embracing diversity, which ensures robustness and general- ization to unseen data. In addition to conventional frame- based metrics that evaluate frames independently, we pro- pose a new event-based metric to evaluate detection perfor- mance at event-level. We show how the CoT method con- sistently outperforms state-of-the-art approaches in both frame-based and event-based metrics, across three public datasets that differ in complexity: CK+, FERA and RU- FACS.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Class-Specific Simplex-Latent Dirichlet Allocation for Image Classification [pdf]
Mandar Dixit, Nikhil Rasiwasia, Nuno Vasconcelos |
|
Abstract: An extension of the latent Dirichlet allocation (LDA), denoted class-specific-simplex LDA (css-LDA), is proposed for image classification. An analysis of the supervised LDA models currently used for this task shows that the impact of class information on the topics discovered by these models is very weak in general. This implies that the discovered topics are driven by general image regularities, rather than the semantic regularities of interest for classification. To address this, we introduce a model that induces supervision in topic discovery, while retaining the original flexibility of LDA to account for unanticipated structures of interest. The proposed css-LDA is an LDA model with class supervision at the level of image features. In css-LDA topics are dis- covered per class, i.e. a single set of topics shared across classes is replaced by multiple class-specific topic sets. This model can be used for generative classification using the Bayes decision rule or even extended to discriminative clas- sification with support vector machines (SVMs). A css-LDA model can endow an image with a vector of class and topic specific count statistics that are similar to the Bag-of-words (BoW) histogram. SVM-based discriminants can be learned for classes in the space of these histograms. The effec- tiveness of css-LDA model in both generative and discrim- inative classification frameworks is demonstrated through an extensive experimental evaluation, involving multiple benchmark datasets
|
Similar papers:
[rank all papers by similarity to this]
|
|
Multi-view Object Segmentation in Space and Time [pdf]
Abdelaziz Djelouah, Jean-Sebastien Franco, Edmond Boyer, Francois Le_Clerc, Patrick Perez |
|
Abstract: In this paper, we address the problem of object segmen- tation in multiple views or videos when two or more view- points of the same scene are available. We propose a new approach that propagates segmentation coherence informa- tion in both space and time, hence allowing evidences in one image to be shared over the complete set. To this aim the segmentation is cast as a single efficient labeling prob- lem over space and time with graph cuts. In contrast to most existing multi-view segmentation methods that rely on some form of dense reconstruction, ours only requires a sparse 3D sampling to propagate information between viewpoints. The approach is thoroughly evaluated on standard multi- view datasets, as well as on videos. With static views, results compete with state of the art methods but they are achieved with significantly fewer viewpoints. With multiple videos, we report results that demonstrate the benefit of segmenta- tion propagation through temporal cues.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: Edge detection is a critical component of many vision systems, including object detectors and image segmentation algorithms. Patches of edges exhibit well-known forms of local structure, such as straight lines or T-junctions. In this paper we take advantage of the structure present in local image patches to learn both an accurate and computation- ally efficient edge detector. We formulate the problem of predicting local edge masks in a structured learning frame- work applied to random decision forests. Our novel ap- proach to learning decision trees robustly maps the struc- tured labels to a discrete space on which standard infor- mation gain measures may be evaluated. The result is an approach that obtains realtime performance that is orders of magnitude faster than many competing state-of-the-art approaches, while also achieving state-of-the-art edge de- tection results on the BSDS500 Segmentation dataset and NYU Depth dataset. Finally, we show the potential of our approach as a general purpose edge detector by showing our learned edge models generalize well across datasets.
|
Similar papers:
[rank all papers by similarity to this]
|
|
A Deformable Mixture Parsing Model with Parselets [pdf]
Jian Dong, Qiang Chen, Wei Xia, Zhongyang Huang, Shuicheng Yan |
|
Abstract: In this work, we address the problem of human pars- ing, namely partitioning the human body into semantic re- gions, by using the novel Parselet representation. Previous works often consider solving the problem of human pose estimation as the prerequisite of human parsing. We ar- gue that these approaches cannot obtain optimal pixel level parsing due to the inconsistent targets between these tasks. In this paper, we propose to use Parselets as the build- ing blocks of our parsing model. Parselets are a group of parsable segments which can generally be obtained by low- level over-segmentation algorithms and bear strong seman- tic meaning. We then build a Deformable Mixture Pars- ing Model (DMPM) for human parsing to simultaneously handle the deformation and multi-modalities of Parselets. The proposed model has two unique characteristics: (1) the possible numerous modalities of Parselet ensembles are ex- hibited as the And-Or structure of sub-trees; (2) to fur- ther solve the practical problem of Parselet occlusion or absence, we directly model the visibility property at some leaf nodes. The DMPM thus directly solves the problem of human parsing by searching for the best graph configura- tion from a pool of Parselet hypotheses without intermediate tasks. Comprehensive evaluations demonstrate the encour- aging performance of the proposed approach.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Stable Hyper-pooling and Query Expansion for Event Detection [pdf]
Matthijs Douze, Jerome Revaud, Cordelia Schmid, Herve Jegou |
|
Abstract: This paper makes two complementary contributions to event retrieval in large collections of videos. First, we propose hyper-pooling strategies that encode the frame de- scriptors into a representation of the video sequence in a stable manner. Our best choices compare favorably with regular pooling techniques based on k-means quantization. Second, we introduce a technique to improve the ranking. It can be interpreted either as a query expansion method or as a similarity adaptation based on the local context of the query video descriptor. Experiments on public bench- marks show that our methods are complementary and im- prove event retrieval results, without sacrificing efficiency.
|
Similar papers:
[rank all papers by similarity to this]
|
|
PixelTrack: A Fast Adaptive Algorithm for Tracking Non-rigid Objects [pdf]
Stefan Duffner, Christophe Garcia |
|
Abstract: In this paper, we present a novel algorithm for fast track- ing of generic objects in videos. The algorithm uses two components: a detector that makes use of the generalised Hough transform with pixel-based descriptors, and a prob- abilistic segmentation method based on global models for foreground and background. These components are used for tracking in a combined way, and they adapt each other in a co-training manner. Through effective model adapta- tion and segmentation, the algorithm is able to track ob- jects that undergo rigid and non-rigid deformations and considerable shape and appearance variations. The pro- posed tracking method has been thoroughly evaluated on challenging standard videos, and outperforms state-of-the- art tracking methods designed for the same task. Finally, the proposed models allow for an extremely efficient imple- mentation, and thus tracking is very fast.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Accurate Blur Models vs. Image Priors in Single Image Super-resolution [pdf]
Netalee Efrat, Daniel Glasner, Alexander Apartsin, Boaz Nadler, Anat Levin |
|
Abstract: Over the past decade, single image Super-Resolution (SR) research has focused on developing sophisticated im- age priors, leading to significant advances. Estimating and incorporating the blur model, that relates the high-res and low-res images, has received much less attention, however. In particular, the reconstruction constraint, namely that the blurred and downsampled high-res output should approxi- mately equal the low-res input image, has been either ig- nored or applied with default fixed blur models. In this work, we examine the relative importance of the image prior and the reconstruction constraint. First, we show that an accurate reconstruction constraint combined with a simple gradient regularization achieves SR results almost as good as those of state-of-the-art algorithms with sophisticated image priors. Second, we study both empirically and the- oretically the sensitivity of SR algorithms to the blur model assumed in the reconstruction constraint. We find that an accurate blur model is more important than a sophisticated image prior. Finally, using real camera data, we demon- strate that the default blur models of various SR algorithms may differ from the camera blur, typically leading to over- smoothed results. Our findings highlight the importance of accurately estimating camera blur in reconstructing raw low- res images acquired by an actual camera.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Restoring an Image Taken through a Window Covered with Dirt or Rain [pdf]
David Eigen, Dilip Krishnan, Rob Fergus |
|
Abstract: Photographs taken through a window are often compro- mised by dirt or rain present on the window surface. Com- mon cases of this include pictures taken from inside a ve- hicle, or outdoor security cameras mounted inside a pro- tective enclosure. At capture time, defocus can be used to remove the artifacts, but this relies on achieving a shallow depth-of-field and placement of the camera close to the win- dow. Instead, we present a post-capture image processing solution that can remove localized rain and dirt artifacts from a single image. We collect a dataset of clean/corrupted image pairs which are then used to train a specialized form of convolutional neural network. This learns how to map corrupted image patches to clean ones, implicitly capturing the characteristic appearance of dirt and water droplets in natural images. Our models demonstrate effective removal of dirt and rain in outdoor test conditions.
|
Similar papers:
[rank all papers by similarity to this]
|
|
On the Mean Curvature Flow on Graphs with Applications in Image and Manifold Processing [pdf]
Abdallah El_Chakik, Abderrahim Elmoataz, Ahcene Sadi |
|
Abstract: In this paper, we propose an adaptation and transcrip- tion of the mean curvature level set equation on a general discrete domain (weighted graphs with arbitrary topology). We introduce the perimeters on graph using difference oper- ators and define the curvature as the first variation of these perimeters. Our proposed approach of mean curvature uni- fies both local and non local notions of mean curvature on Euclidean domains. Furthermore, it allows the extension to the processing of manifolds and data which can be repre- sented by graphs.
|
Similar papers:
[rank all papers by similarity to this]
|
|
A Convex Optimization Framework for Active Learning [pdf]
Ehsan Elhamifar, Guillermo Sapiro, Allen Yang, S. Shankar Sasrty |
|
Abstract: In many image/video/web classification problems, we have access to a large number of unlabeled samples. How- ever, it is typically expensive and time consuming to obtain labels for the samples. Active learning is the problem of progressively selecting and annotating the most informa- tive unlabeled samples, in order to obtain a high classi- fication performance. Most existing active learning algo- rithms select only one sample at a time prior to retraining the classifier. Hence, they are computationally expensive and cannot take advantage of parallel labeling systems such as Mechanical Turk. On the other hand, algorithms that allow the selection of multiple samples prior to retraining the classifier, may select samples that have significant in- formation overlap or they involve solving a non-convex op- timization. More importantly, the majority of active learn- ing algorithms are developed for a certain classifier type such as SVM. In this paper, we develop an efficient active learning framework based on convex programming, which can select multiple samples at a time for annotation. Unlike the state of the art, our algorithm can be used in conjunc- tion with any type of classifiers, including those of the fam- ily of the recently proposed Sparse Representation-based Classification (SRC). We use the two principles of classi- fier uncertainty and sample diversity in order to guide the optimization program towards selecting the most informa- tive unlabeled samples, which have th
|
Similar papers:
[rank all papers by similarity to this]
|
|
Write a Classifier: Zero-Shot Learning Using Purely Textual Descriptions [pdf]
Mohamed Elhoseiny, Babak Saleh, Ahmed Elgammal |
|
Abstract: The main question we address in this paper is how to use purely textual description of categories with no training images to learn visual classifiers for these categories. We propose an approach for zero-shot learning of object cat- egories where the description of unseen categories comes in the form of typical text such as an encyclopedia entry, without the need to explicitly defined attributes. We pro- pose and investigate two baseline formulations, based on regression and domain adaptation. Then, we propose a new constrained optimization formulation that combines a re- gression function and a knowledge transfer function with additional constraints to predict the classifier parameters for new classes. We applied the proposed approach on two fine-grained categorization datasets, and the results indi- cate successful classifier prediction.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: The vast majority of work on motion segmentation adopts the affine camera model due to its simplicity. Under the affine model, the motion segmentation problem becomes that of subspace separation. Due to this assumption, such methods are mainly offline and exhibit poor performance when the assumption is not satisfied. This is made evident in state-of-the-art methods that relax this assumption by using piecewise affine spaces and spectral clustering techniques to achieve better results. In this paper, we formulate the problem of motion segmentation as that of manifold sep- aration. We then show how label propagation can be used in an online framework to achieve manifold separation. The performance of our framework is evaluated on a benchmark dataset and achieves competitive performance while being online.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: We propose a fundamentally novel approach to real-time visual odometry for a monocular camera. It allows to ben- efit from the simplicity and accuracy of dense tracking which does not depend on visual features while running in real-time on a CPU. The key idea is to continuously esti- mate a semi-dense inverse depth map for the current frame, which in turn is used to track the motion of the camera using dense image alignment. More specifically, we estimate the depth of all pixels which have a non-negligible image gradi- ent. Each estimate is represented as a Gaussian probability distribution over the inverse depth. We propagate this in- formation over time, and update it with new measurements as new images arrive. In terms of tracking accuracy and computational speed, the proposed method compares favor- ably to both state-of-the-art dense and feature-based visual odometry and SLAM algorithms. As our method runs in real-time on a CPU, it is of large practical value for robotics and augmented reality applications.
1. Towards Dense Monocular Visual Odometry
Tracking a hand-held camera and recovering the three- dimensional structure of the environment in real-time is among the most prominent challenges in computer vision. In the last years, dense approaches to these challenges have become increasingly popular: Instead of operating solely on visual feature positions, they reconstruct and track on the whole image using a surface-based map and thereby are fundamentally different
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: We extend patch based methods to work on patches in 3D space. We start with Coherency Sensitive Hashing [12] (CSH), which is an algorithm for matching patches between two RGB images, and extend it to work with RGBD im- ages. This is done by warping all 3D patches to a com- mon virtual plane in which CSH is performed. To avoid noise due to warping of patches of various normals and depths, we estimate a group of dominant planes and com- pute CSH on each plane separately, before merging the matching patches. The result is DCSH - an algorithm that matches world (3D) patches in order to guide the search for image plane matches. An independent contribution is an ex- tension of CSH, which we term Social-CSH. It allows a ma- jor speedup of the k nearest neighbor (kNN) version of CSH - its runtime growing linearly, rather than quadratically, in k. Social-CSH is used as a subcomponent of DCSH when many NNs are required, as in the case of image denoising. We show the benefits of using depth information to image re- construction and image denoising, demonstrated on several RGBD images.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: Given a set of images which share an object from the same semantic category, we would like to co-segment the shared object. We define good co-segments to be ones which can be easily composed (like a puzzle) from large pieces of other co-segments, yet are difficult to compose from remaining image parts. These pieces must not only match well but also be statistically significant (hard to com- pose at random). This gives rise to co-segmentation of ob- jects in very challenging scenarios with large variations in appearance, shape and large amounts of clutter. We further show how multiple images can collaborate and score each others co-segments to improve the overall fidelity and accuracy of the co-segmentation. Our co-segmentation can be applied both to large image collections, as well as to very few images (where there is too little data for unsupervised learning). At the extreme, it can be applied even to a single image, to extract its co-occurring objects. Our approach obtains state-of-the-art results on benchmark datasets. We further show very encouraging co-segmentation results on the challenging PASCAL-VOC dataset.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Relative Attributes for Large-Scale Abandoned Object Detection [pdf]
Quanfu Fan, Prasad Gabbur, Sharath Pankanti |
|
Abstract: Effective reduction of false alarms in large-scale video surveillance is rather challenging, especially for applica- tions where abnormal events of interest rarely occur, such as abandoned object detection. We develop an approach to prioritize alerts by ranking them, and demonstrate its great effectiveness in reducing false positives while keep- ing good detection accuracy. Our approach benefits from a novel representation of abandoned object alerts by relative attributes, namely staticness, foregroundness and abandon- ment. The relative strengths of these attributes are quan- tified using a ranking function[19] learnt on suitably de- signed low-level spatial and temporal features.These at- tributes of varying strengths are not only powerful in dis- tinguishing abandoned objects from false alarms such as people and light artifacts, but also computationally efficient for large-scale deployment. With these features, we apply a linear ranking algorithm to sort alerts according to their relevance to the end-user. We test the effectiveness of our approach on both public data sets and large ones collected from the real world.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: This paper proposes to learn binary hash codes within a statistical learning framework, in which an upper bound of the probability of Bayes decision errors is derived for different forms of hash functions and a rigorous proof of the convergence of the upper bound is presented. Conse- quently, minimizing such an upper bound leads to consistent performance improvements of existing hash code learning algorithms, regardless of whether original algorithms are unsupervised or supervised. This paper also illustrates a fast hash coding method that exploits simple binary tests to achieve orders of magnitude improvement in coding speed as compared to projection based methods.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Unbiased Metric Learning: On the Utilization of Multiple Datasets and Web Images for Softening Bias [pdf]
Chen Fang, Ye Xu, Daniel N. Rockmore |
|
Abstract: Many standard computer vision datasets exhibit biases due to a variety of sources including illumination condi- tion, imaging system, and preference of dataset collectors. Biases like these can have downstream effects in the use of vision datasets in the construction of generalizable tech- niques, especially for the goal of the creation of a classifi- cation system capable of generalizing to unseen and novel datasets. In this work we propose Unbiased Metric Learn- ing (UML), a metric learning approach, to achieve this goal. UML operates in the following two steps: (1) By varying hyperparameters, it learns a set of less biased can- didate distance metrics on training examples from multiple biased datasets. The key idea is to learn a neighborhood for each example, which consists of not only examples of the same category from the same dataset, but those from other datasets. The learning framework is based on structural SVM. (2) We do model validation on a set of weakly-labeled web images retrieved by issuing class labels as keywords to search engine. The metric with best validation performance is selected. Although the web images sometimes have noisy labels, they often tend to be less biased, which makes them suitable for the validation set in our task. Cross-dataset im- age classification experiments are carried out. Results show significant performance improvement on four well-known computer vision datasets.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Large-Scale Image Annotation by Efficient and Robust Kernel Metric Learning [pdf]
Zheyun Feng, Rong Jin, Anil Jain |
|
Abstract: One of the key challenges in search-based image anno- tation models is to define an appropriate similarity mea- sure between images. Many kernel distance metric learning (KML) algorithms have been developed in order to capture the nonlinear relationships between visual features and se- mantics of the images. One fundamental limitation in apply- ing KML to image annotation is that it requires converting image annotations into binary constraints, leading to a sig- nificant information loss. In addition, most KML algorithms suffer from high computational cost due to the requirement that the learned matrix has to be positive semi-definitive (PSD). In this paper, we propose a robust kernel metric learning (RKML) algorithm based on the regression tech- nique that is able to directly utilize image annotations. The proposed method is also computationally more efficient be- cause PSD property is automatically ensured by regression. We provide the theoretical guarantee for the proposed algo- rithm, and verify its efficiency and effectiveness for image annotation by comparing it to state-of-the-art approaches for both distance metric learning and image annotation.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Super-resolution via Transform-Invariant Group-Sparse Regularization [pdf]
Carlos Fernandez-Granda, Emmanuel J. Candes |
|
Abstract: We present a framework to super-resolve planar regions found in urban scenes and other man-made environments by taking into account their 3D geometry. Such regions have highly structured straight edges, but this prior is challeng- ing to exploit due to deformations induced by the projection onto the imaging plane. Our method factors out such de- formations by using recently developed tools based on con- vex optimization to learn a transform that maps the image to a domain where its gradient has a simple group-sparse structure. This allows to obtain a novel convex regularizer that enforces global consistency constraints between the edges of the image. Computational experiments with real images show that this data-driven approach to the design of regularizers promoting transform-invariant group spar- sity is very effective at high super-resolution factors. We view our approach as complementary to most recent super- resolution methods, which tend to focus on hallucinating high-frequency textures.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Mining Multiple Queries for Image Retrieval: On-the-Fly Learning of an Object-Specific Mid-level Representation [pdf]
Basura Fernando, Tinne Tuytelaars |
|
Abstract: In this paper we present a new method for object re- trieval starting from multiple query images. The use of mul- tiple queries allows for a more expressive formulation of the query object including, e.g., different viewpoints and/or viewing conditions. This, in turn, leads to more diverse and more accurate retrieval results. When no query images are available to the user, they can easily be retrieved from the internet using a standard image search engine.
In particular, we propose a new method based on pattern mining. Using the minimal description length principle, we derive the most suitable set of patterns to describe the query object, with patterns corresponding to local feature config- urations. This results in a powerful object-specific mid-level image representation.
The archive can then be searched efficiently for similar images based on this representation, using a combination of two inverted file systems. Since the patterns already encode local spatial information, good results on several standard image retrieval datasets are obtained even without costly re-ranking based on geometric verification.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Unsupervised Visual Domain Adaptation Using Subspace Alignment [pdf]
Basura Fernando, Amaury Habrard, Marc Sebban, Tinne Tuytelaars |
|
Abstract: In this paper, we introduce a new domain adaptation (DA) algorithm where the source and target domains are represented by subspaces described by eigenvectors. In this context, our method seeks a domain adaptation solution by learning a mapping function which aligns the source sub- space with the target one. We show that the solution of the corresponding optimization problem can be obtained in a simple closed form, leading to an extremely fast algorithm. We use a theoretical result to tune the unique hyperparam- eter corresponding to the size of the subspaces. We run our method on various datasets and show that, despite its intrin- sic simplicity, it outperforms state of the art DA methods.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Image Guided Depth Upsampling Using Anisotropic Total Generalized Variation [pdf]
David Ferstl, Christian Reinbacher, Rene Ranftl, Matthias Ruether, Horst Bischof |
|
Abstract: In this work we present a novel method for the chal- lenging problem of depth image upsampling. Modern depth cameras such as Kinect or Time of Flight cameras deliver dense, high quality depth measurements but are limited in their lateral resolution. To overcome this limitation we for- mulate a convex optimization problem using higher order regularization for depth image upsampling. In this opti- mization an anisotropic diffusion tensor, calculated from a high resolution intensity image, is used to guide the upsam- pling. We derive a numerical algorithm based on a primal- dual formulation that is efficiently parallelized and runs at multiple frames per second. We show that this novel up- sampling clearly outperforms state of the art approaches in terms of speed and accuracy on the widely used Middlebury 2007 datasets. Furthermore, we introduce novel datasets with highly accurate groundtruth, which, for the first time, enable to benchmark depth upsampling methods using real sensor data.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: 13 IEEE International Conference on Computer Vision
'SVVIGXIH1SQIRX -PPYQMRERX )WXMQEXMSR
%FWXVEGX
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
|
Similar papers:
[rank all papers by similarity to this]
|
|
Structured Learning of Sum-of-Submodular Higher Order Energy Functions [pdf]
Alexander Fix, Thorsten Joachims, Sam Park, Ramin Zabih |
|
Abstract: Submodular functions can be exactly minimized in poly- nomial time, and the special case that graph cuts solve with max flow [19] has had significant impact in computer vi- sion [5, 21, 28]. In this paper we address the important class of sum-of-submodular (SoS) functions [2, 18], which can be efficiently minimized via a variant of max flow called submodular flow [6]. SoS functions can naturally express higher order priors involving, e.g., local image patches; however, it is difficult to fully exploit their expressive power because they have so many parameters. Rather than trying to formulate existing higher order priors as an SoS func- tion, we take a discriminative learning approach, effectively searching the space of SoS functions for a higher order prior that performs well on our training set. We adopt a structural SVM approach [15, 34] and formulate the train- ing problem in terms of quadratic programming; as a re- sult we can efficiently search the space of SoS priors via an extended cutting-plane algorithm. We also show how the state-of-the-art max flow method for vision problems [11] can be modified to efficiently solve the submodular flow problem. Experimental comparisons are made against the OpenCV implementation of the GrabCut interactive seg- mentation technique [28], which uses hand-tuned parame- ters instead of machine learning. On a standard dataset [12] our method learns higher order priors with hundreds of parameter values, and produces significantly better s
|
Similar papers:
[rank all papers by similarity to this]
|
|
Data-Driven 3D Primitives for Single Image Understanding [pdf]
David F. Fouhey, Abhinav Gupta, Martial Hebert |
|
Abstract: What primitives should we use to infer the rich 3D world behind an image? We argue that these primitives should be both visually discriminative and geometrically informa- tive and we present a technique for discovering such primi- tives. We demonstrate the utility of our primitives by using them to infer 3D surface normals given a single image. Our technique substantially outperforms the state-of-the-art and shows improved cross-dataset performance.
|
Similar papers:
[rank all papers by similarity to this]
|
|
EVSAC: Accelerating Hypotheses Generation by Modeling Matching Scores with Extreme Value Theory [pdf]
Victor Fragoso, Pradeep Sen, Sergio Rodriguez, Matthew Turk |
|
Abstract: Algorithms based on RANSAC that estimate models us- ing feature correspondences between images can slow down tremendously when the percentage of correct correspon- dences (inliers) is small. In this paper, we present a prob- abilistic parametric model that allows us to assign confi- dence values for each matching correspondence and there- fore accelerates the generation of hypothesis models for RANSAC under these conditions. Our framework lever- ages Extreme Value Theory to accurately model the statis- tics of matching scores produced by a nearest-neighbor fea- ture matcher. Using a new algorithm based on this model, we are able to estimate accurate hypotheses with RANSAC at low inlier ratios significantly faster than previous state- of-the-art approaches, while still performing comparably when the number of inliers is large. We present results of ho- mography and fundamental matrix estimation experiments for both SIFT and SURF matches that demonstrate that our method leads to accurate and fast model estimations.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Separating Reflective and Fluorescent Components Using High Frequency Illumination in the Spectral Domain [pdf]
Ying Fu, Antony Lam, Imari Sato, Takahiro Okabe, Yoichi Sato |
|
Abstract: Hyperspectral imaging is beneficial to many applica- tions but current methods do not consider fluorescent effects which are present in everyday items ranging from paper, to clothing, to even our food. Furthermore, everyday fluores- cent items exhibit a mix of reflectance and fluorescence. So proper separation of these components is necessary for an- alyzing them. In this paper, we demonstrate efficient sep- aration and recovery of reflective and fluorescent emission spectra through the use of high frequency illumination in the spectral domain. With the obtained fluorescent emis- sion spectra from our high frequency illuminants, we then present to our knowledge, the first method for estimating the fluorescent absorption spectrum of a material given its emission spectrum. Conventional bispectral measurement of absorption and emission spectra needs to examine all combinations of incident and observed light wavelengths. In contrast, our method requires only two hyperspectral im- ages. The effectiveness of our proposed methods are then evaluated through a combination of simulation and real experiments. We also demonstrate an application of our method to synthetic relighting of real scenes.
|
Similar papers:
[rank all papers by similarity to this]
|
|
A Unified Video Segmentation Benchmark: Annotation, Metrics and Analysis [pdf]
Fabio Galasso, Naveen Shankar Nagaraja, Tatiana Jimenez Cardenas, Thomas Brox, Bernt Schiele |
|
Abstract: Video segmentation research is currently limited by the lack of a benchmark dataset that covers the large variety of subproblems appearing in video segmentation and that is large enough to avoid overfitting. Consequently, there is little analysis of video segmentation which generalizes across subtasks, and it is not yet clear which and how video segmentation should leverage the information from the still-frames, as previously studied in image segmenta- tion, alongside video specific information, such as temporal volume, motion and occlusion. In this work we provide such an analysis based on annotations of a large video dataset, where each video is manually segmented by multiple per- sons. Moreover, we introduce a new volume-based metric that includes the important aspect of temporal consistency, that can deal with segmentation hierarchies, and that re- flects the tradeoff between over-segmentation and segmen- tation accuracy.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: Modern descriptors like HOG and SIFT are now com- monly used in vision for pattern detection within im- age and video. From a signal processing perspective, this detection process can be efficiently posed as a cor- relation/convolution between a multi-channel image and a multi-channel detector/filter which results in a single- channel response map indicating where the pattern (e.g. object) has occurred. In this paper, we propose a novel framework for learning a multi-channel detector/filter ef- ficiently in the frequency domain, both in terms of training time and memory footprint, which we refer to as a multi- channel correlation filter. To demonstrate the effectiveness of our strategy, we evaluate it across a number of visual de- tection/localization tasks where we: (i) exhibit superior per- formance to current state of the art correlation filters, and (ii) superior computational and memory efficiencies com- pared to state of the art spatial detectors.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: We aim to decompose a global histogram representation of an image into histograms of its associated objects and re- gions. This task is formulated as an optimization problem, given a set of linear classifiers, which can effectively dis- criminate the object categories present in the image. Our decomposition bypasses harder problems associated with accurately localizing and segmenting objects. We evaluate our method on a wide variety of composite histograms, and also compare it with MRF-based solutions. In addition to merely measuring the accuracy of decomposition, we also show the utility of the estimated object and background his- tograms for the task of image classification on the PASCAL VOC 2007 dataset.
|
Similar papers:
[rank all papers by similarity to this]
|
|
A Color Constancy Model with Double-Opponency Mechanisms [pdf]
Shaobing Gao, Kaifu Yang, Chaoyi Li, Yongjie Li |
|
Abstract: The double-opponent color-sensitive cells in the primary visual cortex (V1) of the human visual system (HVS) have long been recognized as the physiological basis of color constancy. We introduce a new color constancy model by imitating the functional properties of the HVS from the reti- na to the double-opponent cells in V1. The idea behind the model originates from the observation that the color distri- bution of the responses of double-opponent cells to the input color-biased images coincides well with the light source di- rection. Then the true illuminant color of a scene is easily estimated by searching for the maxima of the separate RGB channels of the responses of double-opponent cells in the RGB space. Our systematical experimental evaluations on two commonly used image datasets show that the proposed model can produce competitive results in comparison to the complex state-of-the-art approaches, but with a simple im- plementation and without the need for training.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Discriminant Tracking Using Tensor Representation with Semi-supervised Improvement [pdf]
Jin Gao, Junliang Xing, Weiming Hu, Steve Maybank |
|
Abstract: Visual tracking has witnessed growing methods in objec- t representation, which is crucial to robust tracking. The dominant mechanism in object representation is using im- age features encoded in a vector as observations to perform tracking, without considering that an image is intrinsically a matrix, or a 2nd-order tensor. Thus approaches following this mechanism inevitably lose a lot of useful information, and therefore cannot fully exploit the spatial correlation- s within the 2D image ensembles. In this paper, we ad- dress an image as a 2nd-order tensor in its original form, and find a discriminative linear embedding space approxi- mation to the original nonlinear submanifold embedded in the tensor space based on the graph embedding framework. We specially design two graphs for characterizing the in- trinsic local geometrical structure of the tensor space, so as to retain more discriminant information when reducing the dimension along certain tensor dimensions. However, spatial correlations within a tensor are not limited to the el- ements along these dimensions. This means that some part of the discriminant information may not be encoded in the embedding space. We introduce a novel technique called semi-supervised improvement to iteratively adjust the em- bedding space to compensate for the loss of discriminant information, hence improving the performance of our track- er. Experimental results on challenging videos demonstrate the effectiveness and robustness of the prop
|
Similar papers:
[rank all papers by similarity to this]
|
|
Fine-Grained Categorization by Alignments [pdf]
E. Gavves, B. Fernando, C.G.M. Snoek, A.W.M. Smeulders, T. Tuytelaars |
|
Abstract: The aim of this paper is fine-grained categorization with- out human interaction. Different from prior work, which relies on detectors for specific object parts, we propose to localize distinctive details by roughly aligning the objects using just the overall shape, since implicit to fine-grained categorization is the existence of a super-class shape shared among all classes. The alignments are then used to trans- fer part annotations from training images to test images (supervised alignment), or to blindly yet consistently seg- ment the object in a number of regions (unsupervised align- ment). We furthermore argue that in the distinction of fine- grained sub-categories, classification-oriented encodings like Fisher vectors are better suited for describing local- ized information than popular matching oriented features like HOG. We evaluate the method on the CU-2011 Birds and Stanford Dogs fine-grained datasets, outperforming the state-of-the-art.
|
Similar papers:
[rank all papers by similarity to this]
|
|
SIFTpack: A Compact Representation for Efficient SIFT Matching [pdf]
Alexandra Gilinsky, Lihi Zelnik Manor |
|
Abstract: Computing distances between large sets of SIFT descrip- tors is a basic step in numerous algorithms in computer vision. When the number of descriptors is large, as is of- ten the case, computing these distances can be extremely time consuming. In this paper we propose the SIFTpack: a compact way of storing SIFT descriptors, which enables significantly faster calculations between sets of SIFTs than the current solutions. SIFTpack can be used to represent SIFTs densely extracted from a single image or sparsely from multiple different images. We show that the SIFTpack representation saves both storage space and run time, for both finding nearest neighbors and for computing all dis- tances between all descriptors. The usefulness of SIFTpack is also demonstrated as an alternative implementation for K-means dictionaries of visual words.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: In this paper, we show how to train a deformable part model (DPM) fasttypically in less than 20 minutes, or four times faster than the current fastest methodwhile maintaining high average precision on the PASCAL VOC datasets. At the core of our approach is latent LDA, a novel generalization of linear discriminant analysis for learning latent variable models. Unlike latent SVM, latent LDA uses efficient closed-form updates and does not re- quire an expensive search for hard negative examples. Our approach also acts as a springboard for a detailed experi- mental study of DPM training. We isolate and quantify the impact of key training factors for the first time (e.g., How important are discriminative SVM filters? How important is joint parameter estimation? How many negative images are needed for training?). Our findings yield useful insights for researchers working with Markov random fields and part- based models, and have practical implications for speeding up tasks such as model selection.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Hidden Factor Analysis for Age Invariant Face Recognition [pdf]
Dihong Gong, Zhifeng Li, Dahua Lin, Jianzhuang Liu, Xiaoou Tang |
|
Abstract: Age invariant face recognition has received increasing attention due to its great potential in real world applica- tions. In spite of the great progress in face recognition tech- niques, reliably recognizing faces across ages remains a dif- ficult task. The facial appearance of a person changes sub- stantially over time, resulting in significant intra-class vari- ations. Hence, the key to tackle this problem is to separate the variation caused by aging from the person-specific fea- tures that are stable. Specifically, we propose a new method, called Hidden Factor Analysis (HFA). This method captures the intuition above through a probabilistic model with two latent factors: an identity factor that is age-invariant and an age factor affected by the aging process. Then, the ob- served appearance can be modeled as a combination of the components generated based on these factors. We also de- velop a learning algorithm that jointly estimates the latent factors and the model parameters using an EM procedure. Extensive experiments on two well-known public domain face aging datasets: MORPH (the largest public face ag- ing database) and FGNET, clearly show that the proposed method achieves notable improvement over state-of-the-art algorithms.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: The problem of minimizing the Potts energy function frequently occurs in computer vision applications. One way to tackle this NP-hard problem was proposed by Kov- tun [20, 21]. It identifies a part of an optimal solution by running k maxflow computations, where k is the number of labels. The number of labeled pixels can be significant in some applications, e.g. 50-93% in our tests for stereo. We show how to reduce the runtime to O(log k) maxflow com- putations (or one parametric maxflow computation). Fur- thermore, the output of our algorithm allows to speed-up the subsequent alpha expansion for the unlabeled part, or can be used as it is for time-critical applications.
To derive our technique, we generalize the algorithm of Felzenszwalb et al. [7] for Tree Metrics. We also show a connection to k-submodular functions from combinato- rial optimization, and discuss k-submodular relaxations for general energy functions.
|
Similar papers:
[rank all papers by similarity to this]
|
|
YouTube2Text: Recognizing and Describing Arbitrary Activities Using Semantic Hierarchies and Zero-Shot Recognition [pdf]
Sergio Guadarrama, Niveda Krishnamoorthy, Girish Malkarnenkar, Subhashini Venugopalan, Raymond Mooney, Trevor Darrell, Kate Saenko |
|
Abstract: Despite a recent push towards large-scale object recog- nition, activity recognition remains limited to narrow do- mains and small vocabularies of actions. In this paper, we tackle the challenge of recognizing and describing activi- ties in-the-wild. We present a solution that takes a short video clip and outputs a brief sentence that sums up the main activity in the video, such as the actor, the action and its object. Unlike previous work, our approach works on out-of-domain actions: it does not require training videos of the exact activity. If it cannot find an accurate prediction for a pre-trained model, it finds a less specific answer that is also plausible from a pragmatic standpoint. We use se- mantic hierarchies learned from the data to help to choose an appropriate level of generalization, and priors learned from web-scale natural language corpora to penalize un- likely combinations of actors/actions/objects; we also use a web-scale language model to fill in novel verbs, i.e. when the verb does not appear in the training set. We evaluate our method on a large YouTube corpus and demonstrate it is able to generate short sentence descriptions of video clips better than baseline approaches.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: Digital images nowadays show large appearance vari- abilities on picture styles, in terms of color tone, contrast, vignetting, and etc. These picture styles are directly re- lated to the scene radiance, image pipeline of the camera, and post processing functions (e.g., photography effect fil- ters). Due to the complexity and nonlinearity of these fac- tors, popular gradient-based image descriptors generally are not invariant to different picture styles, which could de- grade the performance for object recognition. Given that images shared online or created by individual users are taken with a wide range of devices and may be processed by various post processing functions, to find a robust ob- ject recognition system is useful and challenging. In this paper, we investigate the influence of picture styles on ob- ject recognition by making a connection between image de- scriptors and a pixel mapping function g, and accordingly propose an adaptive approach based on a g-incorporated kernel descriptor and multiple kernel learning, without es- timating or specifying the image styles used in training and testing. We conduct experiments on the Domain Adaptation data set, the Oxford Flower data set, and several variants of the Flower data set by introducing popular photography effects through post-processing. The results demonstrate that the proposed method consistently yields recognition im- provements over standard descriptors in all studied cases.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: In this paper, we present an approach to predict the extent and height of supporting surfaces such as tables, chairs, and cabinet tops from a single RGBD image. We de- fine support surfaces to be horizontal, planar surfaces that can physically support objects and humans. Given a RGBD image, our goal is to localize the height and full extent of such surfaces in 3D space. To achieve this, we created a labeling tool and annotated 1449 images with rich, com- plete 3D scene models in NYU dataset. We extract ground truth from the annotated dataset and developed a pipeline for predicting floor space, walls, the height and full extent of support surfaces. Finally we match the predicted extent with annotated scenes in training scenes and transfer the the support surface configuration from training scenes. We evaluate the proposed approach in our dataset and demon- strate its effectiveness in understanding scenes in 3D space.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Video Co-segmentation for Meaningful Action Extraction [pdf]
Jiaming Guo, Zhuwen Li, Loong-Fah Cheong, Steven Zhiying Zhou |
|
Abstract: Given a pair of videos having a common action, our goal is to simultaneously segment this pair of videos to extrac- t this common action. As a preprocessing step, we first remove background trajectories by a motion-based figure- ground segmentation. To remove the remaining background and those extraneous actions, we propose the trajectory co- saliency measure, which captures the notion that trajecto- ries recurring in all the videos should have their mutual saliency boosted. This requires a trajectory matching pro- cess which can compare trajectories with different lengths and not necessarily spatiotemporally aligned, and yet be discriminative enough despite significant intra-class varia- tion in the common action. We further leverage the graph matching to enforce geometric coherence between regions so as to reduce feature ambiguity and matching errors. Fi- nally, to classify the trajectories into common action and action outliers, we formulate the problem as a binary la- beling of a Markov Random Field, in which the data term is measured by the trajectory co-saliency and the smooth- ness term is measured by the spatiotemporal consistency between trajectories. To evaluate the performance of our framework, we introduce a dataset containing clips that have animal actions as well as human actions. Experimen- tal results show that the proposed method performs well in common action extraction.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Fibonacci Exposure Bracketing for High Dynamic Range Imaging [pdf]
Mohit Gupta, Daisuke Iso, Shree K. Nayar |
|
Abstract: Exposure bracketing for high dynamic range (HDR) imaging involves capturing several images of the scene at different exposures. If either the camera or the scene moves during capture, the captured images must be registered. Large exposure differences between bracketed images lead to inaccurate registration, resulting in artifacts such as ghosting (multiple copies of scene objects) and blur. We present two techniques, one for image capture (Fibonacci exposure bracketing) and one for image registration (gen- eralized registration), to prevent such motion-related arti- facts. Fibonacci bracketing involves capturing a sequence of images such that each exposure time is the sum of the previous N(N > 1) exposures. Generalized registration involves estimating motion between sums of contiguous sets of frames, instead of between individual frames. Together, the two techniques ensure that motion is always estimated between frames of the same total exposure time. This results in HDR images and videos which have both a large dynamic range and minimal motion-related artifacts. We show, by re- sults for several real-world indoor and outdoor scenes, that the proposed approach significantly outperforms several ex- isting bracketing schemes.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: In this paper, we investigate the properties of Lp norm (p 1) within a projection framework. We start with the KKT equations of the non-linear optimization problem and then use its key properties to arrive at an algorithm for Lp norm projection on the non-negative simplex. We compare with L1 projection which needs prior knowledge of the true norm, as well as hard thresholding based sparsification pro- posed in recent compressed sensing literature. We show performance improvements compared to these techniques across different vision applications.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: Strong ambient illumination severely degrades the per- formance of structured light based techniques. This is espe- cially true in outdoor scenarios, where the structured light sources have to compete with sunlight, whose power is often 2-5 orders of magnitude larger than the projected light. In this paper, we propose the concept of light-concentration to overcome strong ambient illumination. Our key observation is that given a fixed light (power) budget, it is always better to allocate it sequentially in several portions of the scene, as compared to spreading it over the entire scene at once. For a desired level of accuracy, we show that by distributing light appropriately, the proposed approach requires 1-2 or- ders lower acquisition time than existing approaches. Our approach is illumination-adaptive as the optimal light dis- tribution is determined based on a measurement of the am- bient illumination level. Since current light sources have a fixed light distribution, we have built a prototype light source that supports flexible light distribution by control- ling the scanning speed of a laser scanner. We show several high quality 3D scanning results in a wide range of outdoor scenarios. The proposed approach will benefit 3D vision systems that need to operate outdoors under extreme ambi- ent illumination levels on a limited time and power budget.
|
Similar papers:
[rank all papers by similarity to this]
|
|
The Interestingness of Images [pdf]
Michael Gygli, Helmut Grabner, Hayko Riemenschneider, Fabian Nater, Luc Van_Gool |
|
Abstract: We investigate human interest in photos. Based on our own and others psychological experiments, we identify var- ious cues for interestingness, namely aesthetics, unusu- alness and general preferences. For the ranking of retrieved images, interestingness is more appropriate than cues pro- posed earlier. Interestingness is, for example, correlated with what people believe they will remember. This is op- posed to actual memorability, which is uncorrelated to both of them. We introduce a set of features computationally capturing the three main aspects of visual interestingness that we propose and build an interestingness predictor from them. Its performance is shown on three datasets with vary- ing context, reflecting diverse levels of prior knowledge of the viewers.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: This paper presents a new method for deblurring photos using a sharp reference example that contains some shared content with the blurry photo. Most previous deblurring methods that exploit information from other photos require an accurately registered photo of the same static scene. In contrast, our method aims to exploit reference images where the shared content may have undergone substantial photo- metric and non-rigid geometric transformations, as these are the kind of reference images most likely to be found in personal photo albums.
Our approach builds upon a recent method for example- based deblurring using non-rigid dense correspondence (NRDC) [11] and extends it in two ways. First, we suggest exploiting information from the reference image not only for blur kernel estimation, but also as a powerful local prior for the non-blind deconvolution step. Second, we introduce a simple yet robust technique for spatially varying blur es- timation, rather than assuming spatially uniform blur. Un- like the above previous method, which has proven successful only with simple deblurring scenarios, we demonstrate that our method succeeds on a variety of real-world examples. We provide quantitative and qualitative evaluation of our method and show that it outperforms the state-of-the-art.
|
Similar papers:
[rank all papers by similarity to this]
|
|
High Quality Shape from a Single RGB-D Image under Uncalibrated Natural Illumination [pdf]
Yudeog Han, Joon-Young Lee, In So Kweon |
|
Abstract: We present a novel framework to estimate detailed shape of diffuse objects with uniform albedo from a single RGB-D image. To estimate accurate lighting in natural illumination environment, we introduce a general lighting model consist- ing of two components: global and local models. The global lighting model is estimated from the RGB-D input using the low-dimensional characteristic of a diffuse reflectance model. The local lighting model represents spatially vary- ing illumination and it is estimated by using the smoothly- varying characteristic of illumination. With both the global and local lighting model, we can estimate complex light- ing variations in uncontrolled natural illumination condi- tions accurately. For high quality shape capture, a shape- from-shading approach is applied with the estimated light- ing model. Since the entire process is done with a single RGB-D input, our method is capable of capturing the high quality shape details of a dynamic object under natural illu- mination. Experimental results demonstrate the feasibility and effectiveness of our method that dramatically improves shape details of the rough depth input.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Dictionary Learning and Sparse Coding on Grassmann Manifolds: An Extrinsic Solution [pdf]
Mehrtash Harandi, Conrad Sanderson, Chunhua Shen, Brian Lovell |
|
Abstract: Recent advances in computer vision and machine learning suggest that a wide range of problems can be addressed more appropriately by considering non-Euclidean geome- try. In this paper we explore sparse dictionary learning over the space of linear subspaces, which form Riemannian structures known as Grassmann manifolds. To this end, we propose to embed Grassmann manifolds into the space of symmetric matrices by an isometric mapping, which en- ables us to devise a closed-form solution for updating a Grassmann dictionary, atom by atom. Furthermore, to han- dle non-linearity in data, we propose a kernelised version of the dictionary learning algorithm. Experiments on sev- eral classification tasks (face recognition, action recogni- tion, dynamic texture classification) show that the proposed approach achieves considerable improvements in discrim- ination accuracy, in comparison to state-of-the-art meth- ods such as kernelised Affine Hull Method and graph- embedding Grassmann discriminant analysis.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: We present a data-driven method for estimating the 3D shapes of faces viewed in single, unconstrained photos (aka in-the-wild). Our method was designed with an empha- sis on robustness and efficiency with the explicit goal of deployment in real-world applications which reconstruct and display faces in 3D. Our key observation is that for many practical applications, warping the shape of a ref- erence face to match the appearance of a query, is enough to produce realistic impressions of the querys 3D shape. Doing so, however, requires matching visual features be- tween the (possibly very different) query and reference im- ages, while ensuring that a plausible face shape is pro- duced. To this end, we describe an optimization process which seeks to maximize the similarity of appearances and depths, jointly, to those of a reference model. We describe our system for monocular face shape reconstruction and present both qualitative and quantitative experiments, com- paring our method against alternative systems, and demon- strating its capabilities. Finally, as a testament to its suit- ability for real-world applications, we offer an open, on- line implementation of our system, providing unique means of instant 3D viewing of faces appearing in web photos.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: We present an image editing tool called Content-Aware Rotation. Casually shot photos can appear tilted, and are often corrected by rotation and cropping. This trivial so- lution may remove desired content and hurt image integri- ty. Instead of doing rigid rotation, we propose a warping method that creates the perception of rotation and avoids cropping. Human vision studies suggest that the perception of rotation is mainly due to horizontal/vertical lines. We de- sign an optimization-based method that preserves the rota- tion of horizontal/vertical lines, maintains the completeness of the image content, and reduces the warping distortion. An efficient algorithm is developed to address the challeng- ing optimization. We demonstrate our content-aware rota- tion method on a variety of practical cases.
|
Similar papers:
[rank all papers by similarity to this]
|
|
PM-Huber: PatchMatch with Huber Regularization for Stereo Matching [pdf]
Philipp Heise, Sebastian Klose, Brian Jensen, Alois Knoll |
|
Abstract: Most stereo correspondence algorithms match support windows at integer-valued disparities and assume a con- stant disparity value within the support window. The re- cently proposed PatchMatch stereo algorithm [7] over- comes this limitation of previous algorithms by directly esti- mating planes. This work presents a method that integrates the PatchMatch stereo algorithm into a variational smooth- ing formulation using quadratic relaxation. The resulting algorithm allows the explicit regularization of the disparity and normal gradients using the estimated plane parame- ters. Evaluation of our method in the Middlebury bench- mark shows that our method outperforms the traditional integer-valued disparity strategy as well as the original al- gorithm and its variants in sub-pixel accurate disparity es- timation.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Real-Time Body Tracking with One Depth Camera and Inertial Sensors [pdf]
Thomas Helten, Meinard Muller, Hans-Peter Seidel, Christian Theobalt |
|
Abstract: In recent years, the availability of inexpensive depth cameras, such as the Microsoft Kinect, has boosted the re- search in monocular full body skeletal pose tracking. Un- fortunately, existing trackers often fail to capture poses where a single camera provides insufficient data, such as non-frontal poses, and all other poses with body part oc- clusions. In this paper, we present a novel sensor fusion ap- proach for real-time full body tracking that succeeds in such difficult situations. It takes inspiration from previous track- ing solutions, and combines a generative tracker and a dis- criminative tracker retrieving closest poses in a database. In contrast to previous work, both trackers employ data from a low number of inexpensive body-worn inertial sen- sors. These sensors provide reliable and complementary information when the monocular depth information alone is not sufficient. We also contribute by new algorithmic so- lutions to best fuse depth and inertial data in both trackers. One is a new visibility model to determine global body pose, occlusions and usable depth correspondences and to decide what data modality to use for discriminative tracking. We also contribute with a new inertial-based pose retrieval, and an adapted late fusion step to calculate the final body pose.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Beyond Hard Negative Mining: Efficient Detector Learning via Block-Circulant Decomposition [pdf]
Joao F. Henriques, Joao Carreira, Rui Caseiro, Jorge Batista |
|
Abstract: Competitive sliding window detectors require vast train- ing sets. Since a pool of natural images provides a nearly endless supply of negative samples, in the form of patches at different scales and locations, training with all the avail- able data is considered impractical. A staple of current ap- proaches is hard negative mining, a method of selecting rel- evant samples, which is nevertheless expensive. Given that samples at slightly different locations have overlapping sup- port, there seems to be an enormous amount of duplicated work. It is natural, then, to ask whether these redundancies can be eliminated.
In this paper, we show that the Gram matrix describing such data is block-circulant. We derive a transformation based on the Fourier transform that block-diagonalizes the Gram matrix, at once eliminating redundancies and parti- tioning the learning problem. This decomposition is valid for any dense features and several learning algorithms, and takes full advantage of modern parallel architectures. Sur- prisingly, it allows training with all the potential samples in sets of thousands of images. By considering the full set, we generate in a single shot the optimal solution, which is usually obtained only after several rounds of hard negative mining. We report speed gains on Caltech Pedestrians and INRIA Pedestrians of over an order of magnitude, allowing training on a desktop computer in a couple of minutes.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Orderless Tracking through Model-Averaged Posterior Estimation [pdf]
Seunghoon Hong, Suha Kwak, Bohyung Han |
|
Abstract: We propose a novel offline tracking algorithm based on model-averaged posterior estimation through patch match- ing across frames. Contrary to existing online and offline tracking methods, our algorithm is not based on temporally- ordered estimates of target state but attempts to select easy- to-track frames first out of the remaining ones without ex- ploiting temporal coherency of target. The posterior of the selected frame is estimated by propagating densities from the already tracked frames in a recursive manner. The den- sity propagation across frames is implemented by an ef- ficient patch matching technique, which is useful for our algorithm since it does not require motion smoothness as- sumption. Also, we present a hierarchical approach, where a small set of key frames are tracked first and non-key frames are handled by local key frames. Our tracking al- gorithm is conceptually well-suited for the sequences with abrupt motion, shot changes, and occlusion. We com- pare our tracking algorithm with existing techniques in real videos with such challenges and illustrate its superior per- formance qualitatively and quantitatively.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Tracking via Robust Multi-task Multi-view Joint Sparse Representation [pdf]
Zhibin Hong, Xue Mei, Danil Prokhorov, Dacheng Tao |
|
Abstract: Combining multiple observation views has proven bene- ficial for tracking. In this paper, we cast tracking as a novel multi-task multi-view sparse learning problem and exploit the cues from multiple views including various types of visual features, such as intensity, color, and edge, where each feature observation can be sparsely repre- sented by a linear combination of atoms from an adaptive feature dictionary. The proposed method is integrated in a particle filter framework where every view in each particle is regarded as an individual task. We jointly consider the underlying relationship between tasks across different views and different particles, and tackle it in a unified robust multi-task formulation. In addition, to capture the frequently emerging outlier tasks, we decompose the repre- sentation matrix to two collaborative components which enable a more robust and accurate approximation. We show that the proposed formulation can be efficiently solved using the Accelerated Proximal Gradient method with a small number of closed-form updates. The presented tracker is implemented using four types of features and is tested on numerous benchmark video sequences. Both the qualitative and quantitative results demonstrate the superior perfor- mance of the proposed approach compared to several state- of-the-art trackers.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Multi-scale Topological Features for Hand Posture Representation and Analysis [pdf]
Kaoning Hu, Lijun Yin |
|
Abstract: In this paper, we propose a multi-scale topological fea- ture representation for automatic analysis of hand pos- ture. Such topological features have the advantage of be- ing posture-dependent while being preserved under certain variations of illumination, rotation, personal dependency, etc. Our method studies the topology of the holes between the hand region and its convex hull. Inspired by the princi- ple of Persistent Homology, which is the theory of computa- tional topology for topological feature analysis over multi- ple scales, we construct the multi-scale Betti Numbers ma- trix (MSBNM) for the topological feature representation. In our experiments, we used 12 different hand postures and compared our features with three popular features (HOG, MCT, and Shape Context) on different data sets. In addition to hand postures, we also extend the feature representations to arm postures. The results demonstrate the feasibility and reliability of the proposed method.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Recognising Human-Object Interaction via Exemplar Based Modelling [pdf]
Jian-Fang Hu, Wei-Shi Zheng, Jianhuang Lai, Shaogang Gong, Tao Xiang |
|
Abstract: Human action can be recognised from a single still im- age by modelling Human-object interaction (HOI), which infers the mutual spatial structure information between hu- man and object as well as their appearance. Existing ap- proaches rely heavily on accurate detection of human and object, and estimation of human pose. They are thus sensi- tive to large variations of human poses, occlusion and un- satisfactory detection of small size objects. To overcome this limitation, a novel exemplar based approach is pro- posed in this work. Our approach learns a set of spatial pose-object interaction exemplars, which are density func- tions describing how a person is interacting with a manip- ulated object for different activities spatially in a proba- bilistic way. A representation based on our HOI exemplar thus has great potential for being robust to the errors in human/object detection and pose estimation. A new frame- work consists of a proposed exemplar based HOI descrip- tor and an activity specific matching model that learns the parameters is formulated for robust human activity recog- nition. Experiments on two benchmark activity datasets demonstrate that the proposed approach obtains state-of- the-art performance.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Collaborative Active Learning of a Kernel Machine Ensemble for Recognition [pdf]
Gang Hua, Chengjiang Long, Ming Yang, Yan Gao |
|
Abstract: Active learning is an effective way of engaging users to interactively train models for visual recognition. The vast majority of previous works, if not all of them, focused on active learning with a single human oracle. The problem of active learning with multiple oracles in a collaborative setting has not been well explored. Moreover, most of the previous works assume that the labels provided by the hu- man oracles are noise free, which may often be violated in reality. We present a collaborative computational model for active learning with multiple human oracles. It leads to not only an ensemble kernel machine that is robust to label noises, but also a principled label quality measure to online detect irresponsible labelers. Instead of running indepen- dent active learning processes for each individual human oracle, our model captures the inherent correlations among the labelers through shared data among them. Our simula- tion experiments and experiments with real crowd-sourced noisy labels demonstrated the efficacy of our model.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Coupled Dictionary and Feature Space Learning with Applications to Cross-Domain Image Synthesis and Recognition [pdf]
De-An Huang, Yu-Chiang Frank Wang |
|
Abstract: Cross-domain image synthesis and recognition are typi- cally considered as two distinct tasks in the areas of com- puter vision and pattern recognition. Therefore, it is not clear whether approaches addressing one task can be eas- ily generalized or extended for solving the other. In this paper, we propose a unified model for coupled dictionary and feature space learning. The proposed learning model not only observes a common feature space for associating cross-domain image data for recognition purposes, the de- rived feature space is able to jointly update the dictionaries in each image domain for improved representation. This is why our method can be applied to both cross-domain image synthesis and recognition problems. Experiments on a vari- ety of synthesis and recognition tasks such as single image super-resolution, cross-view action recognition, and sketch- to-photo face recognition would verify the effectiveness of our proposed learning model.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Coupling Alignments with Recognition for Still-to-Video Face Recognition [pdf]
Zhiwu Huang, Xiaowei Zhao, Shiguang Shan, Ruiping Wang, Xilin Chen |
|
Abstract: The Still-to-Video (S2V) face recognition systems typi- cally need to match faces in low-quality videos captured un- der unconstrained conditions against high quality still face images, which is very challenging because of noise, image blur, low face resolutions, varying head pose, complex light- ing, and alignment difficulty. To address the problem, one solution is to select the frames of best quality from videos (hereinafter called quality alignment in this paper). Mean- while, the faces in the selected frames should also be geo- metrically aligned to the still faces offline well-aligned in the gallery. In this paper, we discover that the interactions among the three tasksquality alignment, geometric align- ment and face recognitioncan benefit from each other, thus should be performed jointly. With this in mind, we propose a Coupling Alignments with Recognition (CAR) method to tightly couple these tasks via low-rank regularized sparse representation in a unified framework. Our method makes the three tasks promote mutually by a joint optimization in an Augmented Lagrange Multiplier routine. Extensive experiments on two challenging S2V datasets demonstrate that our method outperforms the state-of-the-art methods impressively.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Text Localization in Natural Images Using Stroke Feature Transform and Text Covariance Descriptors [pdf]
Weilin Huang, Zhe Lin, Jianchao Yang, Jue Wang |
|
Abstract: In this paper, we present a new approach for text lo- calization in natural images, by discriminating text and non-text regions at three levels: pixel, component and text- line levels. Firstly, a powerful low-level filter called the Stroke Feature Transform (SFT) is proposed, which extends the widely-used Stroke Width Transform (SWT) by incor- porating color cues of text pixels, leading to significantly enhanced performance on inter-component separation and intra-component connection. Secondly, based on the out- put of SFT, we apply two classifiers, a text component clas- sifier and a text-line classifier, sequentially to extract text regions, eliminating the heuristic procedures that are com- monly used in previous approaches. The two classifiers are built upon two novel Text Covariance Descriptors (TCDs) that encode both the heuristic properties and the statisti- cal characteristics of text stokes. Finally, text regions are located by simply thresholding the text-line confident map. Our method was evaluated on two benchmark datasets: ICDAR 2005 and ICDAR 2011, and the corresponding F- measure values are 0.72 and 0.73, respectively, surpassing previous methods in accuracy by a large margin.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Optimal Orthogonal Basis and Image Assimilation: Motion Modeling [pdf]
Etienne Huot, Giuseppe Papari, Isabelle Herlin |
|
Abstract: This paper describes modeling and numerical computa- tion of orthogonal bases, which are used to describe im- ages and motion fields. Motion estimation from image data is then studied on subspaces spanned by these bases. A reduced model is obtained as the Galerkin projection on these subspaces of a physical model, based on Euler and optical flow equations. A data assimilation method is stud- ied, which assimilates coefficients of image data in the re- duced model in order to estimate motion coefficients. The approach is first quantified on synthetic data: it demon- strates the interest of model reduction as a compromise be- tween results quality and computational cost. Results ob- tained on real data are then displayed so as to illustrate the method.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Markov Network-Based Unified Classifier for Face Identification [pdf]
Wonjun Hwang, Kyungshik Roh, Junmo Kim |
|
Abstract: We propose a novel unifying framework using a Markov network to learn the relationship between multiple classi- fiers in face recognition. We assume that we have several complementary classifiers and assign observation nodes to the features of a query image and hidden nodes to the fea- tures of gallery images. We connect each hidden node to its corresponding observation node and to the hidden nodes of other neighboring classifiers. For each observation-hidden node pair, we collect a set of gallery candidates that are most similar to the observation instance, and the relation- ship between the hidden nodes is captured in terms of the similarity matrix between the collected gallery images. Pos- terior probabilities in the hidden nodes are computed by the belief-propagation algorithm. The novelty of the pro- posed framework is the method that takes into account the classifier dependency using the results of each neighbor- ing classifier. We present extensive results on two different evaluation protocols, known and unknown image variation tests, using three different databases, which shows that the proposed framework always leads to good accuracy in face recognition.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: Assigning a visual code to a low-level image descrip- tor, which we call code assignment, is the most computa- tionally expensive part of image classification algorithms based on the bag of visual word (BoW) framework. This paper proposes a fast computation method, Neighbor-to- Neighbor (NTN) search, for this code assignment. Based on the fact that image features from an adjacent region are usually similar to each other, this algorithm effectively re- duces the cost of calculating the distance between a code- word and a feature vector. This method can be applied not only to a hard codebook constructed by vector quantization (NTN-VQ), but also to a soft codebook, a Gaussian mix- ture model (NTN-GMM). We evaluated this method on the PASCAL VOC 2007 classification challenge task. NTN-VQ reduced the assignment cost by 77.4% in super-vector cod- ing, and NTN-GMM reduced it by 89.3% in Fisher-vector coding, without any significant degradation in classification performance.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Scene Collaging: Analysis and Synthesis of Natural Images with Semantic Layers [pdf]
Phillip Isola, Ce Liu |
|
Abstract: To quickly synthesize complex scenes, digital artists of-
ten collage together visual elements from multiple sources:
for example, mountains from New Zealand behind a Scottish
castle with wisps of Saharan sand in front. In this paper, we
propose to use a similar process in order to parse a scene.
We model a scene as a collage of warped, layered objects
sampled from labeled, reference images. Each object is re-
lated to the rest by a set of support constraints. Scene pars-
ing is achieved through analysis-by-synthesis. Starting with
a dataset of labeled exemplar scenes, we retrieve a dictio-
Ce Liu Microsoft Research celiu@microsoft.com
1$234%+5()#%
*+",&$(-.%&/%0"#$#0%
!"#$#%"&''()#%
15()#%#6+,$)% 15()#74&7($()'.28% 0.$48#0+0%
Original image Edited image
nary of candidate object segments that match a query im-
9($6&5%0"#$#%%
Original image Edited image
age. We then combine elements of this set into a scene col- lage that explains the query image. Beyond just assigning object labels to pixels, scene collaging produces a lot more information such as the number of each type of object in the scene, how they support one another, the ordinal depth of each object, and, to some degree, occluded content. We exploit this representation for several applications: image editing, random scene synthesis, and image-to-anaglyph.
|
Similar papers:
[rank all papers by similarity to this]
|
|
What is the Most EfficientWay to Select Nearest Neighbor Candidates for Fast Approximate Nearest Neighbor Search? [pdf]
Masakazu Iwamura, Tomokazu Sato, Koichi Kise |
|
Abstract: Approximate nearest neighbor search (ANNS) is a basic and important technique used in many tasks such as object recognition. It involves two processes: selecting nearest neighbor candidates and performing a brute-force search of these candidates. Only the former though has scope for improvement. In most existing methods, it approximates the space by quantization. It then calculates all the distances between the query and all the quantized values (e.g., clus- ters or bit sequences), and selects a fixed number of can- didates close to the query. The performance of the method is evaluated based on accuracy as a function of the num- ber of candidates. This evaluation seems rational but poses a serious problem; it ignores the computational cost of the process of selection. In this paper, we propose a new ANNS method that takes into account costs in the selection pro- cess. Whereas existing methods employ computationally expensive techniques such as comparative sort and heap, the proposed method does not. This realizes a significantly more efficient search. We have succeeded in reducing com- putation times by one-third compared with the state-of-the- art on an experiment using 100 million SIFT features.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Real-World Normal Map Capture for Nearly Flat Reflective Surfaces [pdf]
Bastien Jacquet, Christian Hane, Kevin Koser, Marc Pollefeys |
|
Abstract: Although specular objects have gained interest in recent years, virtually no approaches exist for markerless recon- struction of reflective scenes in the wild. In this work, we present a practical approach to capturing normal maps in real-world scenes using video only. We focus on nearly pla- nar surfaces such as windows, facades from glass or metal, or frames, screens and other indoor objects and show how normal maps of these can be obtained without the use of an artificial calibration object. Rather, we track the reflections of real-world straight lines, while moving with a hand-held or vehicle-mounted camera in front of the object. In con- trast to error-prone local edge tracking, we obtain the re- flections by a robust, global segmentation technique of an ortho-rectified 3D video cube that also naturally allows ef- ficient user interaction. Then, at each point of the reflective surface, the resulting 2D-curve to 3D-line correspondence provides a novel quadratic constraint on the local surface normal. This allows to globally solve for the shape by in- tegrability and smoothness constraints and easily supports the usage of multiple lines. We demonstrate the technique on several objects and facades.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: Classification cascades have been very effective for ob- ject detection. Such a cascade fails to perform well in data domains with variations in appearances that may not be captured in the training examples. This limited generaliza- tion severely restricts the domains for which they can be used effectively. A common approach to address this limi- tation is to train a new cascade of classifiers from scratch for each of the new domains. Building separate detectors for each of the different domains requires huge annotation and computational effort, making it not scalable to a large number of data domains. Here we present an algorithm for quickly adapting a pre-trained cascade of classifiers using a small number of labeled positive instances from a different yet similar data domain. In our experiments with images of human babies and human-like characters from movies, we demonstrate that the adapted cascade significantly outper- forms both of the original cascade and the one trained from scratch using the given training examples.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Coarse-to-Fine Semantic Video Segmentation Using Supervoxel Trees [pdf]
Aastha Jain, Shuanak Chatterjee, Rene Vidal |
|
Abstract: We propose an exact, general and efficient coarse-to-fine energy minimization strategy for semantic video segmenta- tion. Our strategy is based on a hierarchical abstraction of the supervoxel graph that allows us to minimize an energy defined at the finest level of the hierarchy by minimizing a series of simpler energies defined over coarser graphs. The strategy is exact, i.e., it produces the same solution as mini- mizing over the finest graph. It is general, i.e., it can be used to minimize any energy function (e.g., unary, pairwise, and higher-order terms) with any existing energy minimization algorithm (e.g., graph cuts and belief propagation). It also gives significant speedups in inference for several datasets with varying degrees of spatio-temporal continuity. We also discuss the strengths and weaknesses of our strategy rela- tive to existing hierarchical approaches, and the kinds of image and video data that provide the best speedups.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: The higher-order clustering problem arises when data is drawn from multiple subspaces or when observations fit a higher-order parametric model. Most solutions to this problem either decompose higher-order similarity measures for use in spectral clustering or explicitly use low-rank ma- trix representations. In this paper we present our approach of Sparse Grassmann Clustering (SGC) that combines at- tributes of both categories. While we decompose the higher- order similarity tensor, we cluster data by directly finding a low dimensional representation without explicitly build- ing a similarity matrix. By exploiting recent advances in online estimation on the Grassmann manifold (GROUSE) we develop an efficient and accurate algorithm that works with individual columns of similarities or partial observa- tions thereof. Since it avoids the storage and decomposition of large similarity matrices, our method is efficient, scal- able and has low memory requirements even for large-scale data. We demonstrate the performance of our SGC method on a variety of segmentation problems including planar seg- mentation of Kinect depth maps and motion segmentation of the Hopkins 155 dataset for which we achieve performance comparable to the state-of-the-art.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Predicting Sufficient Annotation Strength for Interactive Foreground Segmentation [pdf]
Suyog Dutt Jain, Kristen Grauman |
|
Abstract: The mode of manual annotation used in an interactive segmentation algorithm affects both its accuracy and ease- of-use. For example, bounding boxes are fast to supply, yet may be too coarse to get good results on difficult images; freehand outlines are slower to supply and more specific, yet they may be overkill for simple images. Whereas ex- isting methods assume a fixed form of input no matter the image, we propose to predict the tradeoff between accuracy and effort. Our approach learns whether a graph cuts seg- mentation will succeed if initialized with a given annotation mode, based on the images visual separability and fore- ground uncertainty. Using these predictions, we optimize the mode of input requested on new images a user wants segmented. Whether given a single image that should be segmented as quickly as possible, or a batch of images that must be segmented within a specified time budget, we show how to select the easiest modality that will be sufficiently strong to yield high quality segmentations. Extensive results with real users and three datasets demonstrate the impact.
|
Similar papers:
[rank all papers by similarity to this]
|
|
A Framework for Shape Analysis via Hilbert Space Embedding [pdf]
Sadeep Jayasumana, Mathieu Salzmann, Hongdong Li, Mehrtash Harandi |
|
Abstract: We propose a framework for 2D shape analysis using positive definite kernels defined on Kendalls shape mani- fold. Different representations of 2D shapes are known to generate different nonlinear spaces. Due to the nonlinear- ity of these spaces, most existing shape classification algo- rithms resort to nearest neighbor methods and to learning distances on shape spaces. Here, we propose to map shapes on Kendalls shape manifold to a high dimensional Hilbert space where Euclidean geometry applies. To this end, we introduce a kernel on this manifold that permits such a map- ping, and prove its positive definiteness. This kernel lets us extend kernel-based algorithms developed for Euclidean spaces, such as SVM, MKL and kernel PCA, to the shape manifold. We demonstrate the benefits of our approach over the state-of-the-art methods on shape classification, cluster- ing and retrieval.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Fluttering Pattern Generation Using Modified Legendre Sequence for Coded Exposure Imaging [pdf]
Hae-Gon Jeon, Joon-Young Lee, Yudeog Han, Seon Joo Kim, In So Kweon |
|
Abstract: Finding a good binary sequence is critical in determin- ing the performance of the coded exposure imaging, but pre- vious methods mostly rely on a random search for finding the binary codes, which could easily fail to find good long sequences due to the exponentially growing search space. In this paper, we present a new computationally efficient algorithm for generating the binary sequence, which is es- pecially well suited for longer sequences. We show that the concept of the low autocorrelation binary sequence that has been well exploited in the information theory community can be applied for generating the fluttering patterns of the shutter, propose a new measure of a good binary sequence, and present a new algorithm by modifying the Legendre se- quence for the coded exposure imaging. Experiments using both synthetic and real data show that our new algorithm consistently generates better binary sequences for the coded exposure problem, yielding better deblurring and resolution enhancement results compared to the previous methods for generating the binary codes.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Towards Understanding Action Recognition [pdf]
Hueihan Jhuang, Juergen Gall, Silvia Zuffi, Cordelia Schmid, Michael J. Black |
|
Abstract: Although action recognition in videos is widely studied, current methods often fail on real-world datasets. Many re- cent approaches improve accuracy and robustness to cope with challenging video sequences, but it is often unclear what affects the results most. This paper attempts to pro- vide insights based on a systematic performance evalua- tion using thoroughly-annotated data of human actions. We annotate human Joints for the HMDB dataset (J-HMDB). This annotation can be used to derive ground truth optical flow and segmentation. We evaluate current methods using this dataset and systematically replace the output of various algorithms with ground truth. This enables us to discover what is important for example, should we work on improv- ing flow algorithms, estimating human bounding boxes, or enabling pose estimation? In summary, we find that high- level pose features greatly outperform low/mid level fea- tures; in particular, pose over time is critical, but current pose estimation algorithms are not yet reliable enough to provide this information. We also find that the accuracy of a top-performing action recognition framework can be greatly increased by refining the underlying low/mid level features; this suggests it is important to improve optical flow and human detection algorithms. Our analysis and J- HMDB dataset should facilitate a deeper understanding of action recognition algorithms.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: It is known that purely low-level saliency cues such as frequency does not lead to a good salient object detection result, requiring high-level knowledge to be adopted for successful discovery of task-independent salient objects. In this paper, we propose an efficient way to combine such high-level saliency priors and low-level appearance mod- els. We obtain the high-level saliency prior with the object- ness algorithm to find potential object candidates without the need of category information, and then enforce the con- sistency among the salient regions using a Gaussian MRF with the weights scaled by diverse density that emphasizes the influence of potential foreground pixels. Our model ob- tains saliency maps that assign high scores for the whole salient object, and achieves state-of-the-art performance on benchmark datasets covering various foreground statistics.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: Recent years have witnessed the success of large-scale image classification systems that are able to identify ob- jects among thousands of possible labels. However, it is yet unclear how general classifiers such as ones trained on ImageNet can be optimally adapted to specific tasks, each of which only covers a semantically related subset of all the objects in the world. It is inefficient and suboptimal to retrain classifiers whenever a new task is given, and is inapplicable when tasks are not given explicitly, but implic- itly specified as a set of image queries. In this paper we propose a novel probabilistic model that jointly identifies the underlying task and performs prediction with a linear- time probabilistic inference algorithm, given a set of query images from a latent task. We present efficient ways to esti- mate parameters for the model, and an open-source toolbox to train classifiers distributedly at a large scale. Empirical results based on the ImageNet data showed significant per- formance increase over several baseline algorithms.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: We present a linear method for global camera pose reg- istration from pairwise relative poses encoded in essential matrices. Our method minimizes an approximate geomet- ric error to enforce the triangular relationship in camera triplets. This formulation does not suffer from the typi- cal unbalanced scale problem in linear methods relying on pairwise translation direction constraints, i.e. an alge- braic error; nor the system degeneracy from collinear mo- tion. In the case of three cameras, our method provides a good linear approximation of the trifocal tensor. It can be directly scaled up to register multiple cameras. The re- sults obtained are accurate for point triangulation and can serve as a good initialization for final bundle adjustment. We evaluate the algorithm performance with different types of data and demonstrate its effectiveness. Our system pro- duces good accuracy, robustness, and outperforms some well-known systems on efficiency.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Saliency Detection via Absorbing Markov Chain [pdf]
Bowen Jiang, Lihe Zhang, Huchuan Lu, Chuan Yang, Ming-Hsuan Yang |
|
Abstract: In this paper, we formulate saliency detection via ab- sorbing Markov chain on an image graph model. We joint- ly consider the appearance divergence and spatial distri- bution of salient objects and the background. The virtual boundary nodes are chosen as the absorbing nodes in a Markov chain and the absorbed time from each transient node to boundary absorbing nodes is computed. The ab- sorbed time of transient node measures its global similar- ity with all absorbing nodes, and thus salient objects can be consistently separated from the background when the absorbed time is used as a metric. Since the time from transient node to absorbing nodes relies on the weights on the path and their spatial distance, the background region on the center of image may be salient. We further exploit the equilibrium distribution in an ergodic Markov chain to reduce the absorbed time in the long-range smooth back- ground regions. Extensive experiments on four benchmark datasets demonstrate robustness and efficiency of the pro- posed method against the state-of-the-art methods.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Salient Region Detection by UFO: Uniqueness, Focusness and Objectness [pdf]
Peng Jiang, Haibin Ling, Jingyi Yu, Jingliang Peng |
|
Abstract: The goal of saliency detection is to locate important pix- els or regions in an image which attract humans visual at- tention the most. This is a fundamental task whose output may serve as the basis for further computer vision tasks like segmentation, resizing, tracking and so forth.
In this paper we propose a novel salient region detec- tion algorithm by integrating three important visual cues namely uniqueness, focusness and objectness (UFO). In particular, uniqueness captures the appearance-derived vi- sual contrast; focusness reflects the fact that salient regions are often photographed in focus; and objectness helps keep completeness of detected salient regions. While uniqueness has been used for saliency detection for long, it is new to integrate focusness and objectness for this purpose. In fac- t, focusness and objectness both provide important salien- cy information complementary of uniqueness. In our ex- periments using public benchmark datasets, we show that, even with a simple pixel level combination of the three com- ponents, the proposed approach yields significant improve- ment compared with previously reported methods.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Complementary Projection Hashing [pdf]
Zhongming Jin, Yao Hu, Yue Lin, Debing Zhang, Shiding Lin, Deng Cai, Xuelong Li |
|
Abstract:
|
Similar papers:
[rank all papers by similarity to this]
|
|
Human Attribute Recognition by Rich Appearance Dictionary [pdf]
Jungseock Joo, Shuo Wang, Song-Chun Zhu |
|
Abstract: We present a part-based approach to the problem of hu- man attribute recognition from a single image of a human body. To recognize the attributes of human from the body parts, it is important to reliably detect the parts. This is a challenging task due to the geometric variation such as articulation and view-point changes as well as the appear- ance variation of the parts arisen from versatile clothing types. The prior works have primarily focused on handling geometric variation by relying on pre-trained part detectors or pose estimators, which require manual part annotation, but the appearance variation has been relatively neglected in these works. This paper explores the importance of the appearance variation, which is directly related to the main task, attribute recognition. To this end, we propose to learn a rich appearance part dictionary of human with signifi- cantly less supervision by decomposing image lattice into overlapping windows at multiscale and iteratively refining local appearance templates. We also present quantitative results in which our proposed method outperforms the ex- isting approaches.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: In underwater environments, cameras need to be con- fined in an underwater housing, viewing the scene through a piece of glass. In case of flat port underwater housings, light rays entering the camera housing are refracted twice, due to different medium densities of water, glass, and air. This causes the usually linear rays of light to bend and the commonly used pinhole camera model to be invalid. When using the pinhole camera model without explicitly model- ing refraction in Structure-from-Motion (SfM) methods, a systematic model error occurs. Therefore, in this paper, we propose a system for computing camera path and 3D points with explicit incorporation of refraction using new meth- ods for pose estimation. Additionally, a new error function is introduced for non-linear optimization, especially bundle adjustment. The proposed method allows to increase recon- struction accuracy and is evaluated in a set of experiments, where the proposed methods performance is compared to SfM with the perspective camera model.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: We address the problem of 3D scene labeling in a struc- tured learning framework. Unlike previous work which uses structured Support Vector Machines, we employ the recently described Decision Tree Field and Regression Tree Field frameworks, which learn the unary and binary terms of a Conditional Random Field from training data. We show this has significant advantages in terms of inference speed, while maintaining similar accuracy. We also demonstrate empirically the importance for overall labeling accuracy of features that make use of prior knowledge about the coarse scene layout such as the location of the ground plane. We show how this coarse layout can be estimated by our frame- work automatically, and that this information can be used to bootstrap improved accuracy in the detailed labeling.
|
Similar papers:
[rank all papers by similarity to this]
|
|
From Where and How to What We See [pdf]
S. Karthikeyan, Vignesh Jagadeesh, Renuka Shenoy, Miguel Ecksteinz, B.S. Manjunath |
|
Abstract: Eye movement studies have confirmed that overt atten- tion is highly biased towards faces and text regions in im- ages. In this paper we explore a novel problem of predicting face and text regions in images using eye tracking data from multiple subjects. The problem is challenging as we aim to predict the semantics (face/text/background) only from eye tracking data without utilizing any image information. The proposed algorithm spatially clusters eye tracking data ob- tained in an image into different coherent groups and sub- sequently models the likelihood of the clusters containing faces and text using a fully connected Markov Random Field (MRF). Given the eye tracking data from a test image, it pre- dicts potential face/head (humans, dogs and cats) and text locations reliably. Furthermore, the approach can be used to select regions of interest for further analysis by object de- tectors for faces and text. The hybrid eye position/object de- tector approach achieves better detection performance and reduced computation time compared to using only the ob- ject detection algorithm. We also present a new eye tracking dataset on 300 images selected from ICDAR, Street-view, Flickr and Oxford-IIIT Pet Dataset from 15 subjects.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Drosophila Embryo Stage Annotation Using Label Propagation [pdf]
Tomas Kazmar, Evgeny Z. Kvon, Alexander Stark, Christoph H. Lampert |
|
Abstract: In this work we propose a system for automatic classi- fication of Drosophila embryos into developmental stages. While the system is designed to solve an actual problem in biological research, we believe that the principle underly- ing it is interesting not only for biologists, but also for re- searchers in computer vision.
The main idea is to combine two orthogonal sources of information: one is a classifier trained on strongly invari- ant features, which makes it applicable to images of very different conditions, but also leads to rather noisy predic- tions. The other is a label propagation step based on a more powerful similarity measure that however is only consistent within specific subsets of the data at a time.
In our biological setup, the information sources are the shape and the staining patterns of embryo images. We show experimentally that while neither of the methods can be used by itself to achieve satisfactory results, their combina- tion achieves prediction quality comparable to human per- formance.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: In this paper we present a new concept of building a mor- phable model directly from photos on the Internet. Mor- phable models have shown very impressive results more than a decade ago, and could potentially have a huge im- pact on all aspects of face modeling and recognition. One of the challenges, however, is to capture and register 3D laser scans of large number of people and facial expres- sions. Nowadays, there are enormous amounts of face pho- tos on the Internet, large portion of which has semantic la- bels. We propose a framework to build a morphable model directly from photos, the framework includes dense regis- tration of Internet photos, as well as, new single view shape reconstruction and modification algorithms.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Modifying the Memorability of Face Photographs [pdf]
Aditya Khosla, Wilma A. Bainbridge, Antonio Torralba, Aude Oliva |
|
Abstract: Contemporary life bombards us with many new images of faces every day, which poses non-trivial constraints on human memory. The vast majority of face photographs are intended to be remembered, either because of personal rel- evance, commercial interests or because the pictures were deliberately designed to be memorable. Can we make a por- trait more memorable or more forgettable automatically? Here, we provide a method to modify the memorability of individual face photographs, while keeping the identity and other facial traits (e.g. age, attractiveness, and emotional magnitude) of the individual fixed. We show that face pho- tographs manipulated to be more memorable (or more for- gettable) are indeed more often remembered (or forgotten) in a crowd-sourcing experiment with an accuracy of 74%. Quantifying and modifying the memorability of a face lends itself to many useful applications in computer vision and graphics, such as mnemonic aids for learning, photo editing applications for social networks and tools for de- signing memorable advertisements.
|
Similar papers:
[rank all papers by similarity to this]
|
|
A Joint Intensity and Depth Co-sparse Analysis Model for Depth Map Super-resolution [pdf]
Martin Kiechle, Simon Hawe, Martin Kleinsteuber |
|
Abstract: High-resolution depth maps can be inferred from low- resolution depth measurements and an additional high- resolution intensity image of the same scene. To that end, we introduce a bimodal co-sparse analysis model, which is able to capture the interdependency of registered intensity and depth information. This model is based on the assump- tion that the co-supports of corresponding bimodal image structures are aligned when computed by a suitable pair of analysis operators. No analytic form of such operators ex- ist and we propose a method for learning them from a set of registered training signals. This learning process is done offline and returns a bimodal analysis operator that is uni- versally applicable to natural scenes. We use this to exploit the bimodal co-sparse analysis model as a prior for solving inverse problems, which leads to an efficient algorithm for depth map super-resolution.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: Scene understanding is an important yet very challeng- ing problem in computer vision. In the past few years, re- searchers have taken advantage of the recent diffusion of depth-RGB (RGB-D) cameras to help simplify the problem of inferring scene semantics. However, while the added 3D geometry is certainly useful to segment out objects with dif- ferent depth values, it also adds complications in that the 3D geometry is often incorrect because of noisy depth mea- surements and the actual 3D extent of the objects is usually unknown because of occlusions. In this paper we propose a new method that allows us to jointly refine the 3D recon- struction of the scene (raw depth values) while accurately segmenting out the objects or scene elements from the 3D reconstruction. This is achieved by introducing a new model which we called Voxel-CRF. The Voxel-CRF model is based on the idea of constructing a conditional random field over a 3D volume of interest which captures the semantic and 3D geometric relationships among different elements (vox- els) of the scene. Such model allows to jointly estimate (1) a dense voxel-based 3D reconstruction and (2) the semantic labels associated with each voxel even in presence of par- tial occlusions using an approximate yet efficient inference strategy. We evaluated our method on the challenging NYU Depth dataset (Version 1 and 2). Experimental results show that our method achieves competitive accuracy in inferring scene semantics and visually appeali
|
Similar papers:
[rank all papers by similarity to this]
|
|
Curvature-Aware Regularization on Riemannian Submanifolds [pdf]
Kwang In Kim, James Tompkin, Christian Theobalt |
|
Abstract: One fundamental assumption in object recognition as well as in other computer vision and pattern recognition problems is that the data generation process lies on a man- ifold and that it respects the intrinsic geometry of the man- ifold. This assumption is held in several successful al- gorithms for diffusion and regularization, in particular, in graph-Laplacian-based algorithms. We claim that the per- formance of existing algorithms can be improved if we ad- ditionally account for how the manifold is embedded within the ambient space, i.e., if we consider the extrinsic geom- etry of the manifold. We present a procedure for charac- terizing the extrinsic (as well as intrinsic) curvature of a manifold M which is described by a sampled point cloud in a high-dimensional Euclidean space. Once estimated, we use this characterization in general diffusion and regular- ization on M , and form a new regularizer on a point cloud. The resulting re-weighted graph Laplacian demonstrates su- perior performance over classical graph Laplacian in semi- supervised learning and spectral clustering.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: Most conventional single image deblurring methods as- sume that the underlying scene is static and the blur is caused by only camera shake. In this paper, in contrast to this restrictive assumption, we address the deblurring problem of general dynamic scenes which contain multi- ple moving objects as well as camera shake. In case of dynamic scenes, moving objects and background have dif- ferent blur motions, so the segmentation of the motion blur is required for deblurring each distinct blur motion accu- rately. Thus, we propose a novel energy model designed with the weighted sum of multiple blur data models, which estimates different motion blurs and their associated pixel- wise weights, and resulting sharp image. In this framework, the local weights are determined adaptively and get high values when the corresponding data models have high data fidelity. And, the weight information is used for the seg- mentation of the motion blur. Non-local regularization of weights are also incorporated to produce more reliable seg- mentation results. A convex optimization-based method is used for the solution of the proposed energy model. Exper- imental results demonstrate that our method outperforms conventional approaches in deblurring both dynamic scenes and static scenes.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Fingerspelling Recognition with Semi-Markov Conditional Random Fields [pdf]
Taehwan Kim, Greg Shakhnarovich, Karen Livescu |
|
Abstract: Recognition of gesture sequences is in general a very dif- ficult problem, but in certain domains the difficulty may be mitigated by exploiting the domains grammar. One such grammatically constrained gesture sequence domain is sign language. In this paper we investigate the case of finger- spelling recognition, which can be very challenging due to the quick, small motions of the fingers. Most prior work on this task has assumed a closed vocabulary of fingerspelled words; here we study the more natural open-vocabulary case, where the only domain knowledge is the possible fin- gerspelled letters and statistics of their sequences. We de- velop a semi-Markov conditional model approach, where feature functions are defined over segments of video and their corresponding letter labels. We use classifiers of let- ters and linguistic handshape features, along with expected motion profiles, to define segmental feature functions. This approach improves letter error rate (Levenshtein distance between hypothesized and correct letter sequences) from 16.3% using a hidden Markov model baseline to 11.6% us- ing the proposed semi-Markov model.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Multi-view 3D Reconstruction from Uncalibrated Radially-Symmetric Cameras [pdf]
Jae-Hak Kim, Yuchao Dai, Hongdong Li, Xin Du, Jonghyuk Kim |
|
Abstract: We present a new multi-view 3D Euclidean reconstruc- tion method for arbitrary uncalibrated radially-symmetric cameras, which needs no calibration or any camera model parameters other than radial symmetry. It is built on the radial 1D camera model [25], a unified mathematical ab- straction to different types of radially-symmetric cameras. We formulate the problem of multi-view reconstruction for radial 1D cameras as a matrix rank minimization prob- lem. Efficient implementation based on alternating direc- tion continuation is proposed to handle scalability issue for real-world applications. Our method applies to a wide range of omnidirectional cameras including both dioptric and catadioptric (central and non-central) cameras. Ad- ditionally, our method deals with complete and incomplete measurements under a unified framework elegantly. Exper- iments on both synthetic and real images from various types of cameras validate the superior performance of our new method, in terms of numerical accuracy and robustness.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Optical Flow via Locally Adaptive Fusion of Complementary Data Costs [pdf]
Tae Hyun Kim, Hee Seok Lee, Kyoung Mu Lee |
|
Abstract: Many state-of-the-art optical flow estimation algorithms optimize the data and regularization terms to solve ill-posed problems. In this paper, in contrast to the conventional op- tical flow framework that uses a single or fixed data model, we study a novel framework that employs locally varying data term that adaptively combines different multiple types of data models. The locally adaptive data term greatly re- duces the matching ambiguity due to the complementary nature of the multiple data models. The optimal number of complementary data models is learnt by minimizing the redundancy among them under the minimum description length constraint (MDL). From these chosen data models, a new optical flow estimation energy model is designed with the weighted sum of the multiple data models, and a convex optimization-based highly effective and practical solution that finds the optical flow, as well as the weights is proposed. Comparative experimental results on the Middlebury opti- cal flow benchmark show that the proposed method using the complementary data models outperforms the state-of- the art methods.
|
Similar papers:
[rank all papers by similarity to this]
|
|
First-Photon Imaging: Scene Depth and Reflectance Acquisition from One Detected Photon per Pixel [pdf]
Ahmed Kirmani, Dongeek Shin, Dheera Venkatraman, Franco N. C. Wong, Vivek K Goyal |
|
Abstract: Capturing depth and reflectance images using active il- lumination despite the detection of little light backscattered from the scene has wide-ranging applications in computer vision. Conventionally, even with single-photon detectors, a large number of detected photons is needed at each pixel location to mitigate Poisson noise. Here, using only the first detected photon at each pixel location, we capture both the 3D structure and reflectivity of the scene, demonstrating greater photon efficiency than previous work. Our com- putational imager combines physically accurate photon- counting statistics with exploitation of spatial correlations present in real-world scenes. We experimentally achieve millimeter-accurate, sub-pulse width depth resolution and 4-bit reflectivity contrast, simultaneously, using only the first photon detection per pixel, even in the presence of high background noise. Our technique enables rapid, low-power, and noise-tolerant active optical imaging.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: We describe a structure-from-motion framework that handles generalized cameras, such as moving rolling- shutter cameras, and works at an unprecedented scale billions of images covering millions of linear kilometers of roadsby exploiting a good relative pose prior along vehicle paths. We exhibit a planet-scale, appearance- augmented point cloud constructed with our framework and demonstrate its practical use in correcting the pose of a street-level image collection.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: This work makes use of a novel, recently proposed epipo- lar constraint for computing the relative pose between two calibrated images. By enforcing the coplanarity of epipo- lar plane normal vectors, it constrains the three degrees of freedom of the relative rotation between two camera views directlyindependently of the translation.
The present paper shows how the approach can be ex- tended to n points, and translated into an efficient eigen- value minimization over the three rotational degrees of free- dom. Each iteration in the non-linear optimization has con- stant execution time, independently of the number of fea- tures. Two global optimization approaches are proposed. The first one consists of an efficient Levenberg-Marquardt scheme with randomized initial value, which already leads to stable and accurate results. The second scheme consists of a globally optimal branch-and-bound algorithm based on a bound on the eigenvalue variation derived from sym- metric eigenvalue-perturbation theory. Analysis of the cost function reveals insights into the nature of a specific rela- tive pose problem, and outlines the complexity under differ- ent conditions. The algorithm shows state-of-the-art perfor- mance w.r.t. essential-matrix based solutions, and a frame- to-frame application to a video sequence immediately leads to an alternative, real-time visual odometry solution.
Note: All algorithms in this paper are made available in the OpenGV library. Please visit http://laurentkneip.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: We present a method to identify and exploit structures that are shared across different object categories, by us- ing sparse coding to learn a shared basis for the part and root templates of Deformable Part Models (DPMs).
Our first contribution consists in using Shift-Invariant Sparse Coding (SISC) to learn mid-level elements that can translate during coding. This results in systematically better approximations than those attained using standard sparse coding. To emphasize that the learned mid-level structures are shiftable we call them shufflets.
Our second contribution consists in using the resulting score to construct probabilistic upper bounds to the exact template scores, instead of taking them at face value as is common in current works. We integrate shufflets in Dual- Tree Branch-and-Bound and cascade-DPMs and demon- strate that we can achieve a substantial acceleration, with practically no loss in performance.
|
Similar papers:
[rank all papers by similarity to this]
|
|
A New Image Quality Metric for Image Auto-denoising [pdf]
Xiangfei Kong, Kuan Li, Qingxiong Yang, Liu Wenyin, Ming-Hsuan Yang |
|
Abstract: This paper proposes a new non-reference image qual- ity metric that can be adopted by the state-of-the-art im- age/video denoising algorithms for auto-denoising. The proposed metric is extremely simple and can be imple- mented in four lines of Matlab code1. The basic assumption employed by the proposed metric is that the noise should be independent of the original image. A direct measure- ment of this dependence is, however, impractical due to the relatively low accuracy of existing denoising method. The proposed metric thus aims at maximizing the structure sim- ilarity between the input noisy image and the estimated im- age noise around homogeneous regions and the structure similarity between the input noisy image and the denoised image around highly-structured regions, and is computed as the linear correlation coefficient of the two correspond- ing structure similarity maps. Numerous experimental re- sults demonstrate that the proposed metric not only out- performs the current state-of-the-art non-reference quality metric quantitatively and qualitatively, but also better main- tains temporal coherence when used for video denoising.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Joint Learning of Discriminative Prototypes and Large Margin Nearest Neighbor Classifiers [pdf]
Martin Kostinger, Paul Wohlhart, Peter M. Roth, Horst Bischof |
|
Abstract: In this paper, we raise important issues concerning the evaluation complexity of existing Mahalanobis metric learning methods. The complexity scales linearly with the size of the dataset. This is especially cumbersome on large scale or for real-time applications with limited time bud- get. To alleviate this problem we propose to represent the dataset by a fixed number of discriminative prototypes. In particular, we introduce a new method that jointly chooses the positioning of prototypes and also optimizes the Ma- halanobis distance metric with respect to these. We show that choosing the positioning of the prototypes and learning the metric in parallel leads to a drastically reduced eval- uation effort while maintaining the discriminative essence of the original dataset. Moreover, for most problems our method performing k-nearest prototype (k-NP) classifica- tion on the condensed dataset leads to even better general- ization compared to k-NN classification using all data. Re- sults on a variety of challenging benchmarks demonstrate the power of our method. These include standard machine learning datasets as well as the challenging Public Fig- ures Face Database. On the competitive machine learning benchmarks we are comparable to the state-of-the-art while being more efficient. On the face benchmark we clearly out- perform the state-of-the-art in Mahalanobis metric learning with drastically reduced evaluation effort.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: Current methods learn monolithic attribute predictors, with the assumption that a single model is sufficient to re- flect human understanding of a visual attribute. However, in reality, humans vary in how they perceive the association between a named property and image content. For example, two people may have slightly different internal models for what makes a shoe look formal, or they may disagree on which of two scenes looks more cluttered. Rather than discount these differences as noise, we propose to learn user-specific attribute models. We adapt a generic model trained with annotations from multiple users, tailoring it to satisfy user-specific labels. Furthermore, we propose novel techniques to infer user-specific labels based on tran- sitivity and contradictions in the users search history. We demonstrate that adapted attributes improve accuracy over both existing monolithic models as well as models that learn from scratch with user-specific data alone. In addition, we show how adapted attributes are useful to personalize im- age search, whether with binary or relative attributes.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Attribute Pivots for Guiding Relevance Feedback in Image Search [pdf]
Adriana Kovashka, Kristen Grauman |
|
Abstract: In interactive image search, a user iteratively refines his results by giving feedback on exemplar images. Active se- lection methods aim to elicit useful feedback, but traditional approaches suffer from expensive selection criteria and cannot predict informativeness reliably due to the impreci- sion of relevance feedback. To address these drawbacks, we propose to actively select pivot exemplars for which feed- back in the form of a visual comparison will most reduce the systems uncertainty. For example, the system might ask, Is your target image more or less crowded than this im- age? Our approach relies on a series of binary search trees in relative attribute space, together with a selection function that predicts the information gain were the user to compare his envisioned target to the next node deeper in a given attributes tree. It makes interactive search more effi- cient than existing strategiesboth in terms of the systems selection time as well as the users feedback effort.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Pose Estimation with Unknown Focal Length Using Points, Directions and Lines [pdf]
Yubin Kuang, Kalle Astrom |
|
Abstract: In this paper, we study the geometry problems of esti- mating camera pose with unknown focal length using com- bination of geometric primitives. We consider points, lines and also rich features such as quivers, i.e. points with one or more directions. We formulate the problems as polyno- mial systems where the constraints for different primitives are handled in a unified way. We develop efficient poly- nomial solvers for each of the derived cases with different combinations of primitives. The availability of these solvers enables robust pose estimation with unknown focal length for wider classes of features. Such rich features allow for fewer feature correspondences and generate larger inlier sets with higher probability. We demonstrate in synthetic experiments that our solvers are fast and numerically sta- ble. For real images, we show that our solvers can be used in RANSAC loops to provide good initial solutions.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Real-Time Solution to the Absolute Pose Problem with Unknown Radial Distortion and Focal Length [pdf]
Zuzana Kukelova, Martin Bujnak, Tomas Pajdla |
|
Abstract: The problem of determining the absolute position and orien- tation of a camera from a set of 2D-to-3D point correspon- dences is one of the most important problems in computer vision with a broad range of applications. In this paper we present a new solution to the absolute pose problem for camera with unknown radial distortion and unknown focal length from five 2D-to-3D point correspondences. Our new solver is numerically more stable, more accurate, and sig- nificantly faster than the existing state-of-the-art minimal four point absolute pose solvers for this problem. Moreover, our solver results in less solutions and can handle larger radial distortions. The new solver is straightforward and uses only simple concepts from linear algebra. Therefore it is simpler than the state-of-the-art Gro bner basis solvers. We compare our new solver with the existing state-of-the- art solvers and show its usefulness on synthetic and real datasets. 1
|
Similar papers:
[rank all papers by similarity to this]
|
|
Discriminative Label Propagation for Multi-object Tracking with Sporadic Appearance Features [pdf]
K.C. Amit Kumar, Christophe De_Vleeschouwer |
|
Abstract: Given a set of plausible detections, detected at each time instant independently, we investigate how to associate them across time. This is done by propagating labels on a set of graphs that capture how the spatio-temporal and the ap- pearance cues promote the assignment of identical or dis- tinct labels to a pair of nodes. The graph construction is driven by the locally linear embedding (LLE) of either the spatio-temporal or the appearance features associated to the detections. Interestingly, the neighborhood of a node in each appearance graph is defined to include all nodes for which the appearance feature is available (except the ones that coexist at the same time). This allows to connect the nodes that share the same appearance even if they are tem- porally distant, which gives our framework the uncommon ability to exploit the appearance features that are available only sporadically along the sequence of detections.
Once the graphs have been defined, the multi-object tracking is formulated as the problem of finding a label as- signment that is consistent with the constraints captured by each of the graphs. This results into a difference of con- vex program that can be efficiently solved. Experiments are performed on a basketball and several well-known pedes- trian datasets in order to validate the effectiveness of the proposed solution.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Camera Alignment Using Trajectory Intersections in Unsynchronized Videos [pdf]
Thomas Kuo, Santhoshkumar Sunderrajan, B.S. Manjunath |
|
Abstract: This paper addresses the novel and challenging prob- lem of aligning camera views that are unsynchronized by low and/or variable frame rates using object trajec- tories. Unlike existing trajectory-based alignment meth- ods, our method does not require frame-to-frame synchro- nization. Instead, we propose using the intersections of corresponding object trajectories to match views. To find these intersections, we introduce a novel trajectory match- ing algorithm based on matching Spatio-Temporal Con- text Graphs (STCGs). These graphs represent the distances between trajectories in time and space within a view, and are matched to an STCG from another view to find the cor- responding trajectories. To the best of our knowledge, this is one of the first attempts to align views that are unsyn- chronized with variable frame rates. The results on simu- lated and real-world datasets show trajectory intersections are a viable feature for camera alignment, and that the tra- jectory matching method performs well in real-world sce- narios.
|
Similar papers:
[rank all papers by similarity to this]
|
|
From Subcategories to Visual Composites: A Multi-level Framework for Object Detection [pdf]
Tian Lan, Michalis Raptis, Leonid Sigal, Greg Mori |
|
Abstract: The appearance of an object changes profoundly with pose, camera view and interactions of the object with other objects in the scene. This makes it challenging to learn de- tectors based on an object-level label (e.g., car). We pos- tulate that having a richer set of labelings (at different levels of granularity) for an object, including finer-grained sub- categories, consistent in appearance and view, and higher- order composites contextual groupings of objects consis- tent in their spatial layout and appearance, can significantly alleviate these problems. However, obtaining such a rich set of annotations, including annotation of an exponentially growing set of object groupings, is simply not feasible.
We propose a weakly-supervised framework for object detection where we discover subcategories and the com- posites automatically with only traditional object-level cat- egory labels as input. To this end, we first propose an exemplar-SVM-based clustering approach, with latent SVM refinement, that discovers a variable length set of discrim- inative subcategories for each object class. We then de- velop a structured model for object detection that captures interactions among object subcategories and automatically discovers semantically meaningful and discriminatively rel- evant visual composites. We show that this model produces state-of-the-art performance on UIUC phrase object detec- tion benchmark.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: This paper introduces a novel similarity learning frame- work. Working with inequality constraints involving quadruplets of images, our approach aims at efficiently modeling similarity from rich or complex semantic label relationships. From these quadruplet-wise constraints, we propose a similarity learning framework relying on a con- vex optimization scheme. We then study how our metric learning scheme can exploit specific class relationships, such as class ranking (relative attributes), and class tax- onomy. We show that classification using the learned met- rics gets improved performance over state-of-the-art meth- ods on several datasets. We also evaluate our approach in a new application to learn similarities between webpage screenshots in a fully unsupervised way.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Detecting Curved Symmetric Parts Using a Deformable Disc Model [pdf]
Tom Sie Ho Lee, Sanja Fidler, Sven Dickinson |
|
Abstract: Symmetry is a powerful shape regularity thats been ex- ploited by perceptual grouping researchers in both human and computer vision to recover part structure from an im- age without a priori knowledge of scene content. Draw- ing on the concept of a medial axis, defined as the locus of centers of maximal inscribed discs that sweep out a sym- metric part, we model part recovery as the search for a sequence of deformable maximal inscribed disc hypothe- ses generated from a multiscale superpixel segmentation, a framework proposed by [13]. However, we learn affinities between adjacent superpixels in a space thats invariant to bending and tapering along the symmetry axis, enabling us to capture a wider class of symmetric parts. Moreover, we introduce a global cost that perceptually integrates the hy- pothesis space by combining a pairwise and a higher-level smoothing term, which we minimize globally using dynamic programming. The new framework is demonstrated on two datasets, and is shown to significantly outperform the base- line [13].
|
Similar papers:
[rank all papers by similarity to this]
|
|
Deterministic Fitting of Multiple Structures Using Iterative MaxFS with Inlier Scale Estimation [pdf]
Kwang Hee Lee, Sang Wook Lee |
|
Abstract: 2013 IEEE International Conference on Computer Vision
!
=<>9*1&. >9
902*&?*&919531*=
1%@0#*&2#*>*&9(:168*=
#! " $ # %
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: In contrast to the current motion segmentation paradigm that assumes independence between the motion subspaces, we approach the motion segmentation problem by seeking the parsimonious basis set that can represent the data. Our formulation explicitly looks for the overlap between sub- spaces in order to achieve a minimal basis representation. This parsimonious basis set is important for the perfor- mance of our model selection scheme because the sharing of basis results in savings of model complexity cost. We propose the use of affinity propagation based method to de- termine the number of motion. The key lies in the incorpo- ration of a global cost model into the factor graph, serving the role of model complexity. The introduction of this global cost model requires additional message update in the factor graph. We derive an efficient update for the new messages associated with this global cost model. An important step in the use of affinity propagation is the subspace hypotheses generation. We use the row-sparse convex proxy solution as an initialization strategy. We further encourage the selec- tion of subspace hypotheses with shared basis by integrat- ing a discount scheme that lowers the factor graph facility cost based on shared basis. We verified the model selection and classification performance of our proposed method on both the original Hopkins 155 dataset and the more bal- anced Hopkins 380 dataset.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Style-Aware Mid-level Representation for Discovering Visual Connections in Space and Time [pdf]
Yong Jae Lee, Alexei A. Efros, Martial Hebert |
|
Abstract: We present a weakly-supervised visual data mining ap- proach that discovers connections between recurring mid- level visual elements in historic (temporal) and geographic (spatial) image collections, and attempts to capture the un- derlying visual style. In contrast to existing discovery meth- ods that mine for patterns that remain visually consistent throughout the dataset, our goal is to discover visual ele- ments whose appearance changes due to change in time or location; i.e., exhibit consistent stylistic variations across the label space (date or geo-location). To discover these elements, we first identify groups of patches that are style- sensitive. We then incrementally build correspondences to find the same element across the entire dataset. Finally, we train style-aware regressors that model each elements range of stylistic differences. We apply our approach to date and geo-location prediction and show substantial improve- ment over several baselines that do not model visual style. We also demonstrate the methods effectiveness on the re- lated task of fine-grained classification.
|
Similar papers:
[rank all papers by similarity to this]
|
|
A Non-parametric Bayesian Network Prior of Human Pose [pdf]
Andreas M. Lehrmann, Peter V. Gehler, Sebastian Nowozin |
|
Abstract: Having a sensible prior of human pose is a vital ingredi- ent for many computer vision applications, including track- ing and pose estimation. While the application of global non-parametric approaches and parametric models has led to some success, finding the right balance in terms of flex- ibility and tractability, as well as estimating model param- eters from data has turned out to be challenging. In this work, we introduce a sparse Bayesian network model of human pose that is non-parametric with respect to the es- timation of both its graph structure and its local distribu- tions. We describe an efficient sampling scheme for our model and show its tractability for the computation of ex- act log-likelihoods. We empirically validate our approach on the Human 3.6M dataset and demonstrate superior per- formance to global models and parametric networks. We further illustrate our models ability to represent and com- pose poses not present in the training set (compositionality) and describe a speed-accuracy trade-off that allows real- time scoring of poses.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Total Variation Regularization for Functions with Values in a Manifold [pdf]
Jan Lellmann, Evgeny Strekalovskiy, Sabrina Koetter, Daniel Cremers |
|
Abstract: While total variation is among the most popular regu- larizers for variational problems, its extension to functions with values in a manifold is an open problem. In this pa- per, we propose the first algorithm to solve such problems which applies to arbitrary Riemannian manifolds. The key idea is to reformulate the variational problem as a multil- abel optimization problem with an infinite number of labels. This leads to a hard optimization problem which can be ap- proximately solved using convex relaxation techniques. The framework can be easily adapted to different manifolds in- cluding spheres and three-dimensional rotations, and al- lows to obtain accurate solutions even with a relatively coarse discretization. With numerous examples we demon- strate that the proposed framework can be applied to varia- tional models that incorporate chromaticity values, normal fields, or camera trajectories.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Locally Affine Sparse-to-Dense Matching for Motion and Occlusion Estimation [pdf]
Marius Leordeanu, Andrei Zanfir, Cristian Sminchisescu |
|
Abstract: Estimating a dense correspondence field between suc- cessive video frames, under large displacement, is impor- tant in many visual learning and recognition tasks. We pro- pose a novel sparse-to-dense matching method for motion field estimation and occlusion detection. As an alterna- tive to the current coarse-to-fine approaches from the op- tical flow literature, we start from the higher level of sparse matching with rich appearance and geometric constraints collected over extended neighborhoods, using an occlusion aware, locally affine model. Then, we move towards the simpler, but denser classic flow field model, with an inter- polation procedure that offers a natural transition between the sparse and the dense correspondence fields. We experi- mentally demonstrate that our appearance features and our complex geometric constraints permit the correct motion es- timation even in difficult cases of large displacements and significant appearance changes. We also propose a novel classification method for occlusion detection that works in conjunction with the sparse-to-dense matching model. We validate our approach on the newly released Sintel dataset and obtain state-of-the-art results.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Sequential Bayesian Model Update under Structured Scene Prior for Semantic Road Scenes Labeling [pdf]
Evgeny Levinkov, Mario Fritz |
|
Abstract: Semantic road labeling is a key component of systems that aim at assisted or even autonomous driving. Consid- ering that such systems continuously operate in the real- world, unforeseen conditions not represented in any con- ceivable training procedure are likely to occur on a regular basis. In order to equip systems with the ability to cope with such situations, we would like to enable adaptation to such new situations and conditions at runtime.
Existing adaptive methods for image labeling either re- quire labeled data from the new condition or even operate globally on a complete test set. None of this is a desirable mode of operation for a system as described above where new images arrive sequentially and conditions may vary.
We study the effect of changing test conditions on scene labeling methods based on a new diverse street scene dataset. We propose a novel approach that can operate in such conditions and is based on a sequential Bayesian model update in order to robustly integrate the arriving im- ages into the adapting procedure.
|
Similar papers:
[rank all papers by similarity to this]
|
|
A Novel Earth Mover's Distance Methodology for Image Matching with Gaussian Mixture Models [pdf]
Peihua Li, Qilong Wang, Lei Zhang |
|
Abstract: The similarity or distance measure between Gaussian mixture models (GMMs) plays a crucial role in content- based image matching. Though the Earth Movers Dis- tance (EMD) has shown its advantages in matching his- togram features, its potentials in matching GMMs remain unclear and are not fully explored. To address this problem, we propose a novel EMD methodology for GMM matching. We rst present a sparse representation based EMD called SR-EMD by exploiting the sparse property of the underly- ing problem. SR-EMD is more efcient and robust than the conventional EMD. Second, we present two novel ground distances between component Gaussians based on the in- formation geometry. The perspective from the Riemannian geometry distinguishes the proposed ground distances from the classical entropy- or divergence-based ones. Further- more, motivated by the success of distance metric learning of vector data, we make the rst attempt to learn the EMD distance metrics between GMMs by using a simple yet ef- fective supervised pair-wise based method. It can adapt the distance metrics between GMMs to specic classica- tion tasks. The proposed method is evaluated on both simu- lated data and benchmark real databases and achieves very promising performance.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Codemaps - Segment, Classify and Search Objects Locally [pdf]
Zhenyang Li, Efstratios Gavves, Koen E.A. van_de_Sande, Cees G.M. Snoek, Arnold W.M. Smeulders |
|
Abstract: In this paper we aim for segmentation and classification of objects. We propose codemaps that are a joint formu- lation of the classification score and the local neighbor- hood it belongs to in the image. We obtain the codemap by reordering the encoding, pooling and classification steps over lattice elements. Other than existing linear decompo- sitions who emphasize only the efficiency benefits for lo- calized search, we make three novel contributions. As a preliminary, we provide a theoretical generalization of the sufficient mathematical conditions under which image en- codings and classification becomes locally decomposable. As first novelty we introduce l2 normalization for arbitrar- ily shaped image regions, which is fast enough for semantic segmentation using our Fisher codemaps. Second, using the same lattice across images, we propose kernel pooling which embeds nonlinearities into codemaps for object clas- sification by explicit or approximate feature mappings. Re- sults demonstrate that l2 normalized Fisher codemaps im- prove the state-of-the-art in semantic segmentation for PAS- CAL VOC. For object classification the addition of nonlin- earities brings us on par with the state-of-the-art, but is 3x faster. Because of the codemaps inherent efficiency, we can reach significant speed-ups for localized search as well. We exploit the efficiency gain for our third novelty: object seg- ment retrieval using a single query image only.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Contextual Hypergraph Modeling for Salient Object Detection [pdf]
Xi Li, Yao Li, Chunhua Shen, Anthony Dick, Anton Van_Den_Hengel |
|
Abstract: Salient object detection aims to locate objects that cap- ture human attention within images. Previous approaches often pose this as a problem of image contrast analysis. In this work, we model an image as a hypergraph that uti- lizes a set of hyperedges to capture the contextual proper- ties of image pixels or regions. As a result, the problem of salient object detection becomes one of finding salient ver- tices and hyperedges in the hypergraph. The main advan- tage of hypergraph modeling is that it takes into account each pixels (or regions) affinity with its neighborhood as well as its separation from image background. Further- more, we propose an alternative approach based on center- versus-surround contextual contrast analysis, which per- forms salient object detection by optimizing a cost-sensitive support vector machine (SVM) objective function. Experi- mental results on four challenging datasets demonstrate the effectiveness of the proposed approaches against the state- of-the-art approaches to salient object detection.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Dynamic Pooling for Complex Event Recognition [pdf]
Weixin Li, Qian Yu, Ajay Divakaran, Nuno Vasconcelos |
|
Abstract: The problem of adaptively selecting pooling regions for the classification of complex video events is considered. Complex events are defined as events composed of several characteristic behaviors, whose temporal configuration can change from sequence to sequence. A dynamic pooling operator is defined so as to enable a unified solution to the problems of event specific video segmentation, tempo- ral structure modeling, and event detection. Video is de- composed into segments, and the segments most informative for detecting a given event are identified, so as to dynami- cally determine the pooling operator most suited for each sequence. This dynamic pooling is implemented by treating the locations of characteristic segments as hidden informa- tion, which is inferred, on a sequence-by-sequence basis, via a large-margin classification rule with latent variables. Although the feasible set of segment selections is combina- torial, it is shown that a globally optimal solution to the inference problem can be obtained efficiently, through the solution of a series of linear programs. Besides the coarse- level location of segments, a finer model of video struc- ture is implemented by jointly pooling features of segment- tuples. Experimental evaluation demonstrates that the re- sulting event detector has state-of-the-art performance on challenging video datasets.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: This paper introduces an automatic method for remov- ing reflection interference when imaging a scene behind a glass surface. Our approach exploits the subtle changes in the reflection with respect to the background in a small set of images taken at slightly different view points. Key to this idea is the use of SIFT-flow to align the images such that a pixel-wise comparison can be made across the input set. Gradients with variation across the image set are assumed to belong to the reflected scenes while constant gradients are assumed to belong to the desired background scene. By correctly labelling gradients belonging to reflection or background, the background scene can be separated from the reflection interference. Unlike previous approaches that exploit motion, our approach does not make any assump- tions regarding the background or reflected scenes geom- etry, nor requires the reflection to be static. This makes our approach practical for use in casual imaging scenar- ios. Our approach is straight forward and produces good results compared with existing methods.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: We present a model for gaze prediction in egocentric video by leveraging the implicit cues that exist in camera wearers behaviors. Specifically, we compute the camera wearers head motion and hand location from the video and combine them to estimate where the eyes look. We further model the dynamic behavior of the gaze, in particular fix- ations, as latent variables to improve the gaze prediction. Our gaze prediction results outperform the state-of-the-art algorithms by a large margin on publicly available egocen- tric vision datasets. In addition, we demonstrate that we get a significant performance boost in recognizing daily actions and segmenting foreground objects by plugging in our gaze predictions into state-of-the-art methods.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Log-Euclidean Kernels for Sparse Representation and Dictionary Learning [pdf]
Peihua Li, Qilong Wang, Wangmeng Zuo, Lei Zhang |
|
Abstract: The symmetric positive denite (SPD) matrices have been widely used in image and vision problems. Recently there are growing interests in studying sparse representa- tion (SR) of SPD matrices, motivated by the great success of SR for vector data. Though the space of SPD matrices is well-known to form a Lie group that is a Riemannian man- ifold, existing work fails to take full advantage of its geo- metric structure. This paper attempts to tackle this problem by proposing a kernel based method for SR and dictionary learning (DL) of SPD matrices. We disclose that the space of SPD matrices, with the operations of logarithmic multi- plication and scalar logarithmic multiplication dened in the Log-Euclidean framework, is a complete inner prod- uct space. We can thus develop a broad family of kernels that satises Mercers condition. These kernels character- ize the geodesic distance and can be computed efciently. We also consider the geometric structure in the DL process by updating atom matrices in the Riemannian space instead of in the Euclidean space. The proposed method is evalu- ated with various vision problems and shows notable per- formance gains over state-of-the-arts.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Model Recommendation with Virtual Probes for Egocentric Hand Detection [pdf]
Cheng Li, Kris M. Kitani |
|
Abstract: Egocentric cameras can be used to benefit such tasks as analyzing fine motor skills, recognizing gestures and learn- ing about hand-object manipulation. To enable such tech- nology, we believe that the hands must detected on the pixel- level to gain important information about the shape of the hands and fingers. We show that the problem of pixel-wise hand detection can be effectively solved, by posing the prob- lem as a model recommendation task. As such, the goal of a recommendation system is to recommend the n-best hand detectors based on the probe set a small amount of la- beled data from the test distribution. This requirement of a probe set is a serious limitation in many applications, such as ego-centric hand detection, where the test distribution may be continually changing. To address this limitation, we propose the use of virtual probes which can be automati- cally extracted from the test distribution. The key idea is that many features, such as the color distribution or rela- tive performance between two detectors, can be used as a proxy to the probe set. In our experiments we show that the recommendation paradigm is well-equipped to handle complex changes in the appearance of the hands in first- person vision. In particular, we show how our system is able to generalize to new scenarios by testing our model across multiple users.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Modeling Occlusion by Discriminative AND-OR Structures [pdf]
Bo Li, Wenze Hu, Tianfu Wu, Song-Chun Zhu |
|
Abstract: Occlusion presents a challenge for detecting objects in real world applications. To address this issue, this paper models object occlusion with an AND-OR structure which (i) represents occlusion at semantic part level, and (ii) cap- tures the regularities of different occlusion configurations (i.e., the different combinations of object part visibilities). This paper focuses on car detection on street. Since anno- tating part occlusion on real images is time-consuming and error-prone, we propose to learn the the AND-OR structure automatically using synthetic images of CAD models placed at different relative positions. The model parameters are learned from real images under the latent structural SVM (LSSVM) framework. In inference, an efficient dynamic pro- gramming (DP) algorithm is utilized. In experiments, we test our method on both car detection and car view estima- tion. Experimental results show that (i) Our CAD simula- tion strategy is capable of generating occlusion patterns for real scenarios, (ii) The proposed AND-OR structure model is effective for modeling occlusions, which outperforms the deformable part-based model (DPM) [6, 10] in car detec- tion on both our self-collected street parking dataset and the Pascal VOC 2007 car dataset [4], (iii) The learned model is on-par with the state-of-the-art methods on car view esti- mation tested on two public datasets.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: This paper demonstrates how the nonlocal principle benefits video matting via the KNN Laplacian, which comes with a straightforward implementation using motion- aware K nearest neighbors. In hindsight, the fundamen- tal problem to solve in video matting is to produce spatio- temporally coherent clusters of moving foreground pixels. When used as described, the motion-aware KNN Lapla- cian is effective in addressing this fundamental problem, as demonstrated by sparse user markups typically on only one frame in a variety of challenging examples featur- ing ambiguous foreground and background colors, chang- ing topologies with disocclusion, significant illumination changes, fast motion, and motion blur. When working with existing Laplacian-based systems, our Laplacian is ex- pected to benefit them immediately with improved clustering of moving foreground pixels.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Perspective Motion Segmentation via Collaborative Clustering [pdf]
Zhuwen Li, Jiaming Guo, Loong-Fah Cheong, Steven Zhiying Zhou |
|
Abstract: This paper addresses real-world challenges in the mo- tion segmentation problem, including perspective effects, missing data, and unknown number of motions. It first for- mulates the 3-D motion segmentation from two perspective views as a subspace clustering problem, utilizing the epipo- lar constraint of an image pair. It then combines the point correspondence information across multiple image frames via a collaborative clustering step, in which tight integra- tion is achieved via a mixed norm optimization scheme. For model selection, we propose an over-segment and merge ap- proach, where the merging step is based on the property of the l1-norm of the mutual sparse representation of two over- segmented groups. The resulting algorithm can deal with incomplete trajectories and perspective effects substantial- ly better than state-of-the-art two-frame and multi-frame methods. Experiments on a 62-clip dataset show the signif- icant superiority of the proposed idea in both segmentation accuracy and model selection.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Probabilistic Elastic Part Model for Unsupervised Face Detector Adaptation [pdf]
Haoxiang Li, Gang Hua, Zhe Lin, Jonathan Brandt, Jianchao Yang |
|
Abstract: We propose an unsupervised detector adaptation algo- rithm to adapt any offline trained face detector to a specific collection of images, and hence achieve better accuracy. The core of our detector adaptation algorithm is a prob- abilistic elastic part (PEP) model, which is offline trained with a set of face examples. It produces a statistically- aligned part based face representation, namely the PEP representation. To adapt a general face detector to a col- lection of images, we compute the PEP representations of the candidate detections from the general face detector, and then train a discriminative classifier with the top positives and negatives. Then we re-rank all the candidate detections with this classifier. This way, a face detector tailored to the statistics of the specific image collection is adapted from the original detector. We present extensive results on three datasets with two state-of-the-art face detectors. The signif- icant improvement of detection accuracy over these state- of-the-art face detectors strongly demonstrates the efficacy of the proposed face detector adaptation algorithm.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Saliency Detection via Dense and Sparse Reconstruction [pdf]
Xiaohui Li, Huchuan Lu, Lihe Zhang, Xiang Ruan, Ming-Hsuan Yang |
|
Abstract: In this paper, we propose a visual saliency detection al- gorithm from the perspective of reconstruction errors. The image boundaries are first extracted via superpixels as like- ly cues for background templates, from which dense and sparse appearance models are constructed. For each im- age region, we first compute dense and sparse reconstruc- tion errors. Second, the reconstruction errors are propa- gated based on the contexts obtained from K-means cluster- ing. Third, pixel-level saliency is computed by an integra- tion of multi-scale reconstruction errors and refined by an object-biased Gaussian model. We apply the Bayes formula to integrate saliency measures based on dense and sparse reconstruction errors. Experimental results show that the proposed algorithm performs favorably against seventeen state-of-the-art methods in terms of precision and recall. In addition, the proposed algorithm is demonstrated to be more effective in highlighting salient objects uniformly and robust to background noise.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Video Segmentation by Tracking Many Figure-Ground Segments [pdf]
Fuxin Li, Taeyoung Kim, Ahmad Humayun, David Tsai, James M. Rehg |
|
Abstract: We propose an unsupervised video segmentation ap- proach by simultaneously tracking multiple holistic figure- ground segments. Segment tracks are initialized from a pool of segment proposals generated from a figure-ground seg- mentation algorithm. Then, online non-local appearance models are trained incrementally for each track using a multi-output regularized least squares formulation. By us- ing the same set of training examples for all segment tracks, a computational trick allows us to track hundreds of seg- ment tracks efficiently, as well as perform optimal online updates in closed-form. Besides, a new composite statisti- cal inference approach is proposed for refining the obtained segment tracks, which breaks down the initial segment pro- posals and recombines for better ones by utilizing high- order statistic estimates from the appearance model and en- forcing temporal consistency. For evaluating the algorithm, a dataset, SegTrack v2, is collected with about 1,000 frames with pixel-level annotations. The proposed framework out- performs state-of-the-art approaches in the dataset, show- ing its efficiency and robustness to challenges in different video sequences.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Joint Segmentation and Pose Tracking of Human in Natural Videos [pdf]
Taegyu Lim, Seunghoon Hong, Bohyung Han, Joon Hee Han |
|
Abstract: We propose an on-line algorithm to extract a human by foreground/background segmentation and estimate pose of the human from the videos captured by moving cameras. We claim that a virtuous cycle can be created by appropri- ate interactions between the two modules to solve individ- ual problems. This joint estimation problem is divided into two subproblems, foreground/background segmentation and pose tracking, which alternate iteratively for optimization; segmentation step generates foreground mask for human pose tracking, and human pose tracking step provides fore- ground response map for segmentation. The final solution is obtained when the iterative procedure converges. We eval- uate our algorithm quantitatively and qualitatively in real videos involving various challenges, and present its out- standing performance compared to the state-of-the-art tech- niques for segmentation and pose estimation.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: We address the problem of localizing and estimating the fine-pose of objects in the image with exact 3D models. Our main focus is to unify contributions from the 1970s with re- cent advances in object detection: use local keypoint de- tectors to find candidate poses and score global alignment of each candidate pose to the image. Moreover, we also provide a new dataset containing fine-aligned objects with their exactly matched 3D models, and a set of models for widely used objects. We also evaluate our algorithm both on object detection and fine pose estimation, and show that our method outperforms state-of-the art algorithms.
|
Similar papers:
[rank all papers by similarity to this]
|
|
3D Sub-query Expansion for Improving Sketch-Based Multi-view Image Retrieval [pdf]
Yen-Liang Lin, Cheng-Yu Huang, Hao-Jeng Wang, Winston Hsu |
|
Abstract: We propose a 3D sub-query expansion approach for boosting sketch-based multi-view image retrieval. The core idea of our method is to automatically convert two (guided) 2D sketches into an approximated 3D sketch model, and then generate multi-view sketches as expanded sub-queries to improve the retrieval performance. To learn the weights among synthesized views (sub-queries), we present a new multi-query feature to model the similarity between sub- queries and dataset images, and formulate it into a convex optimization problem. Our approach shows superior per- formance compared with the state-of-the-art approach on a public multi-view image dataset. Moreover, we also conduct sensitivity tests to analyze the parameters of our approach based on the gathered user sketches.
|
Similar papers:
[rank all papers by similarity to this]
|
|
A General Two-Step Approach to Learning-Based Hashing [pdf]
Guosheng Lin, Chunhua Shen, David Suter, Anton van_den_Hengel |
|
Abstract: Most existing approaches to hashing apply a single form of hash function, and an optimization process which is typ- ically deeply coupled to this specific form. This tight cou- pling restricts the flexibility of the method to respond to the data, and can result in complex optimization problems that are difficult to solve. Here we propose a flexible yet simple framework that is able to accommodate different types of loss functions and hash functions. This framework allows a number of existing approaches to hashing to be placed in context, and simplifies the development of new problem- specific hashing methods. Our framework decomposes the hashing learning problem into two steps: hash bit learning and hash function learning based on the learned bits. The first step can typically be formulated as binary quadratic problems, and the second step can be accomplished by training standard binary classifiers. Both problems have been extensively studied in the literature. Our extensive ex- periments demonstrate that the proposed framework is ef- fective, flexible and outperforms the state-of-the-art.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Characterizing Layouts of Outdoor Scenes Using Spatial Topic Processes [pdf]
Dahua Lin, Jianxiong Xiao |
|
Abstract: In this paper, we develop a generative model to describe the layouts of outdoor scenes the spatial configuration of regions. Specifically, the layout of an image is represented as a composite of regions, each associated with a seman- tic topic. At the heart of this model is a novel stochas- tic process called Spatial Topic Process, which generates a spatial map of topics from a set of coupled Gaussian pro- cesses, thus allowing the distributions of topics to vary con- tinuously across the image plane. A key aspect that distin- guishes this model from previous ones consists in its capa- bility of capturing dependencies across both locations and topics while allowing substantial variations in the layouts. We demonstrate the practical utility of the proposed model by testing it on scene classification, semantic segmentation, and layout hallucination.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Holistic Scene Understanding for 3D Object Detection with RGBD Cameras [pdf]
Dahua Lin, Sanja Fidler, Raquel Urtasun |
|
Abstract: In this paper, we tackle the problem of indoor scene un- derstanding using RGBD data. Towards this goal, we pro- pose a holistic approach that exploits 2D segmentation, 3D geometry, as well as contextual relations between scenes and objects. Specifically, we extend the CPMC [3] frame- work to 3D in order to generate candidate cuboids, and develop a conditional random field to integrate informa- tion from different sources to classify the cuboids. With this formulation, scene classification and 3D object recognition are coupled and can be jointly solved through probabilis- tic inference. We test the effectiveness of our approach on the challenging NYU v2 dataset. The experimental results demonstrate that through effective evidence integration and holistic reasoning, our approach achieves substantial im- provement over the state-of-the-art.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Robust Non-parametric Data Fitting for Correspondence Modeling [pdf]
Wen-Yan Lin, Ming-Ming Cheng, Shuai Zheng, Jiangbo Lu, Nigel Crook |
|
Abstract: We propose a generic method for obtaining non- parametric image warps from noisy point correspondences. Our formulation integrates a huber function into a motion coherence framework. This makes our fitting function es- pecially robust to piecewise correspondence noise (where an image section is consistently mismatched). By utilizing over parameterized curves, we can generate realistic non- parametric image warps from very noisy correspondence. We also demonstrate how our algorithm can be used to help stitch images taken from a panning camera by warping the images onto a virtual push-broom camera imaging plane.
|
Similar papers:
[rank all papers by similarity to this]
|
|
A Scalable Unsupervised Feature Merging Approach to Efficient Dimensionality Reduction of High-Dimensional Visual Data [pdf]
Lingqiao Liu, Lei Wang |
|
Abstract: To achieve a good trade-off between recognition accu- racy and computational efficiency, it is often needed to re- duce high-dimensional visual data to medium-dimensional ones. For this task, even applying a simple full-matrix- based linear projection causes significant computation and memory use. When the number of visual data is large, how to efficiently learn such a projection could even become a problem. The recent feature merging approach offers an ef- ficient way to reduce the dimensionality, which only requires a single scan of features to perform reduction. However, existing merging algorithms do not scale well with high- dimensional data, especially in the unsupervised case.
To address this problem, we formulate unsupervised fea- ture merging as a PCA problem imposed with a special structure constraint. By exploiting its connection with k- means, we transform this constrained PCA problem into a feature clustering problem. Moreover, we employ the hash- ing technique to improve its scalability. These produce a scalable feature merging algorithm for our dimensional- ity reduction task. In addition, we develop an extension of this method by leveraging the neighborhood structure in the data to further improve dimensionality reduction perfor- mance. In further, we explore the incorporation of bipolar merging a variant of merging function which allows the subtraction operation into our algorithms.
Through three applications in visual recognition, we demonstrate that our
|
Similar papers:
[rank all papers by similarity to this]
|
|
Automatic Kronecker Product Model Based Detection of Repeated Patterns in 2D Urban Images [pdf]
Juan Liu, Emmanouil Psarakis, Ioannis Stamos |
|
Abstract: Repeated patterns (such as windows, tiles, balconies and doors) are prominent and significant features in urban scenes. Therefore, detection of these repeated patterns be- comes very important for city scene analysis. This paper attacks the problem of repeated patterns detection in a pre- cise, efficient and automatic way, by combining traditional feature extraction followed by a Kronecker product low- rank modeling approach. Our method is tailored for 2D im- ages of building fac ades. We have developed algorithms for automatic selection of a representative texture within fac ade images using vanishing points and Harris corners. After rectifying the input images, we describe novel algorithms that extract repeated patterns by using Kronecker product based modeling that is based on a solid theoretical founda- tion. Our approach is unique and has not ever been used for fac ade analysis. We have tested our algorithms in a large set of images.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Bird Part Localization Using Exemplar-Based Models with Enforced Pose and Subcategory Consistency [pdf]
Jiongxin Liu, Peter N. Belhumeur |
|
Abstract: In this paper, we propose a novel approach for bird part localization, targeting fine-grained categories with wide variations in appearance due to different poses (including aspect and orientation) and subcategories. As it is chal- lenging to represent such variations across a large set of di- verse samples with tractable parametric models, we turn to individual exemplars. Specifically, we extend the exemplar- based models in [4] by enforcing pose and subcategory consistency at the parts. During training, we build pose- specific detectors scoring part poses across subcategories, and subcategory-specific detectors scoring part appearance across poses. At the testing stage, likely exemplars are matched to the image, suggesting part locations whose pose and subcategory consistency are well-supported by the im- age cues. From these hypotheses, part configuration can be predicted with very high accuracy. Experimental results demonstrate significant performance gains from our method on an extensive dataset: CUB-200-2011 [30], for both lo- calization and classification tasks.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: Shaky stereoscopic video is not only unpleasant to watch but may also cause 3D fatigue. Stabilizing the left and right view of a stereoscopic video separately using a monocu- lar stabilization method tends to both introduce undesir- able vertical disparities and damage horizontal disparities, which may destroy the stereoscopic viewing experience. In this paper, we present a joint subspace stabilization method for stereoscopic video. We prove that the low-rank subspace constraint for monocular video [10] also holds for stereo- scopic video. Particularly, the feature trajectories from the left and right video share the same subspace. Based on this proof, we develop a stereo subspace stabilization method that jointly computes a common subspace from the left and right video and uses it to stabilize the two videos simultane- ously. Our method meets the stereoscopic constraints with- out 3D reconstruction or explicit left-right correspondence. We test our method on a variety of stereoscopic videos with different scene content and camera motion. The experi- ments show that our method achieves high-quality stabiliza- tion for stereoscopic video in a robust and efficient way.
|
Similar papers:
[rank all papers by similarity to this]
|
|
POP: Person Re-identification Post-rank Optimisation [pdf]
Chunxiao Liu, Chen Change Loy, Shaogang Gong, Guijin Wang |
|
Abstract: Owing to visual ambiguities and disparities, person re- identification methods inevitably produce suboptimal rank- list, which still requires exhaustive human eyeballing to identify the correct target from hundreds of different likely- candidates. Existing re-identification studies focus on im- proving the ranking performance, but rarely look into the critical problem of optimising the time-consuming and error-prone post-rank visual search at the user end. In this study, we present a novel one-shot Post-rank OPtimisation (POP) method, which allows a user to quickly refine their search by either one-shot or a couple of sparse negative selections during a re-identification process. We conduct systematic behavioural studies to understand users search- ing behaviour and show that the proposed method allows correct re-identification to converge 2.6 times faster than the conventional exhaustive search. Importantly, through extensive evaluations we demonstrate that the method is ca- pable of achieving significant improvement over the state- of-the-art distance metric learning based ranking models, even with just one shot feedback optimisation, by as much as over 30% performance improvement for rank 1 re- identification on the VIPeR and i-LIDS datasets.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Semantically-Based Human Scanpath Estimation with HMMs [pdf]
Huiying Liu, Dong Xu, Qingming Huang, Wen Li, Min Xu, Stephen Lin |
|
Abstract: We present a method for estimating human scanpaths, which are sequences of gaze shifts that follow visual atten- tion over an image. In this work, scanpaths are modeled based on three principal factors that influence human atten- tion, namely low-level feature saliency, spatial position, and semantic content. Low-level feature saliency is formulated as transition probabilities between different image regions based on feature differences. The effect of spatial position on gaze shifts is modeled as a Levy flight with the shifts following a 2D Cauchy distribution. To account for se- mantic content, we propose to use a Hidden Markov Model (HMM) with a Bag-of-Visual-Words descriptor of image re- gions. An HMM is well-suited for this purpose in that 1) the hidden states, obtained by unsupervised learning, can rep- resent latent semantic concepts, 2) the prior distribution of the hidden states describes visual attraction to the semantic concepts, and 3) the transition probabilities represent hu- man gaze shift patterns. The proposed method is applied to task-driven viewing processes. Experiments and analysis performed on human eye gaze data verify the effectiveness of this method.
|
Similar papers:
[rank all papers by similarity to this]
|
|
SGTD: Structure Gradient and Texture Decorrelating Regularization for Image Decomposition [pdf]
Qiegen Liu, Jianbo Liu, Pei Dong, Dong Liang |
|
Abstract: This paper presents a novel structure gradient and tex- ture decorrelating regularization (SGTD) for image decom- position. The motivation of the idea is under the assumption that the structure gradient and texture components should be properly decorrelated for a successful decomposition. The proposed model consists of the data fidelity term, total variation regularization and the SGTD regularization. An augmented Lagrangian method is proposed to address this optimization issue, by first transforming the unconstrained problem to an equivalent constrained problem and then ap- plying an alternating direction method to iteratively solve the subproblems. Experimental results demonstrate that the proposed method presents better or comparable perfor- mance as state-of-the-art methods do.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Hierarchical Joint Max-Margin Learning of Mid and Top Level Representations for Visual Recognition [pdf]
Hans Lobel, Rene Vidal, Alvaro Soto |
|
Abstract: Currently, Bag-of-Visual-Words (BoVW) and part-based methods are the most popular approaches for visual recog- nition. In both cases, a mid-level representation is built on top of low-level image descriptors and top-level classifiers use this mid-level representation to achieve visual recog- nition. While in current part-based approaches, mid- and top-level representations are usually jointly trained, this is not the usual case for BoVW schemes. A main reason for this is the complex data association problem related to the usual large dictionary size needed by BoVW approaches. As a further observation, typical solutions based on BoVW and part-based representations are usually limited to exten- sions of binary classification schemes, a strategy that ig- nores relevant correlations among classes. In this work we propose a novel hierarchical approach to visual recognition based on a BoVW scheme that jointly learns suitable mid- and top-level representations. Furthermore, using a max- margin learning framework, the proposed approach directly handles the multiclass case at both levels of abstraction. We test our proposed method using several popular bench- mark datasets. As our main result, we demonstrate that, by coupling learning of mid- and top-level representations, the proposed approach fosters sharing of discriminative visual words among target classes, being able to achieve state-of- the-art recognition performance using far less visual words than previous approaches.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Two-Point Gait: Decoupling Gait from Body Shape [pdf]
Stephen Lombardi, Ko Nishino, Yasushi Makihara, Yasushi Yagi |
|
Abstract: Human gait modeling (e.g., for person identification) largely relies on image-based representations that muddle gait with body shape. Silhouettes, for instance, inherently entangle body shape and gait. For gait analysis and recog- nition, decoupling these two factors is desirable. Most im- portant, once decoupled, they can be combined for the task at hand, but not if left entangled in the first place. In this paper, we introduce Two-Point Gait, a gait representation that encodes the limb motions regardless of the body shape. Two-Point Gait is directly computed on the image sequence based on the two point statistics of optical flow fields. We demonstrate its use for exploring the space of human gait and gait recognition under large clothing variation. The results show that we can achieve state-of-the-art person recognition accuracy on a challenging dataset.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Active Visual Recognition with Expertise Estimation in Crowdsourcing [pdf]
Chengjiang Long, Gang Hua, Ashish Kapoor |
|
Abstract: We present a noise resilient probabilistic model for ac- tive learning of a Gaussian process classifier from crowds, i.e., a set of noisy labelers. It explicitly models both the overall label noises and the expertise level of each individ- ual labeler in two levels of flip models. Expectation propa- gation is adopted for efficient approximate Bayesian infer- ence of our probabilistic model for classification, based on which, a generalized EM algorithm is derived to estimate both the global label noise and the expertise of each indi- vidual labeler. The probabilistic nature of our model im- mediately allows the adoption of the prediction entropy and estimated expertise for active selection of data sample to be labeled, and active selection of high quality labelers to la- bel the data, respectively. We apply the proposed model for three visual recognition tasks, i.e, object category recogni- tion, gender recognition, and multi-modal activity recogni- tion, on three datasets with real crowd-sourced labels from Amazon Mechanical Turk. The experiments clearly demon- strated the efficacy of the proposed model.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Transfer Feature Learning with Joint Distribution Adaptation [pdf]
Mingsheng Long, Jianmin Wang, Guiguang Ding, Jiaguang Sun, Philip S. Yu |
|
Abstract: Transfer learning is established as an effective technolo- gy in computer vision for leveraging rich labeled data in the source domain to build an accurate classifier for the target domain. However, most prior methods have not simultane- ously reduced the difference in both the marginal distribu- tion and conditional distribution between domains. In this paper, we put forward a novel transfer learning approach, referred to as Joint Distribution Adaptation (JDA). Specifi- cally, JDA aims to jointly adapt both the marginal distribu- tion and conditional distribution in a principled dimension- ality reduction procedure, and construct new feature repre- sentation that is effective and robust for substantial distribu- tion difference. Extensive experiments verify that JDA can significantly outperform several state-of-the-art methods on four types of cross-domain image classification problems.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: Regression-based techniques have shown promising re- sults for people counting in crowded scenes. However, most existing techniques require expensive and laborious data annotation for model training. In this study, we propose to address this problem from three perspectives: (1) Instead of exhaustively annotating every single frame, the most in- formative frames are selected for annotation automatically and actively. (2) Rather than learning from only labelled data, the abundant unlabelled data are exploited. (3) La- belled data from other scenes are employed to further al- leviate the burden for data annotation. All three ideas are implemented in a unified active and semi-supervised regres- sion framework with ability to perform transfer learning, by exploiting the underlying geometric structure of crowd pat- terns via manifold analysis. Extensive experiments validate the effectiveness of our approach.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: Speedy abnormal event detection meets the growing demand to process an enormous number of surveillance videos. Based on inherent redundancy of video structures, we propose an efficient sparse combination learning frame- work. It achieves decent performance in the detection phase without compromising result quality. The short running time is guaranteed because the new method effectively turns the original complicated problem to one in which only a few costless small-scale least square optimization steps are involved. Our method reaches high detection rates on benchmark datasets at a speed of 140150 frames per second on average when computing on an ordinary desktop PC using MATLAB.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Correlation Adaptive Subspace Segmentation by Trace Lasso [pdf]
Canyi Lu, Jiashi Feng, Zhouchen Lin, Shuicheng Yan |
|
Abstract: This paper studies the subspace segmentation problem. Given a set of data points drawn from a union of subspaces, the goal is to partition them into their underlying subspaces they were drawn from. The spectral clustering method is used as the framework. It requires to find an affinity ma- trix which is close to block diagonal, with nonzero entries corresponding to the data point pairs from the same sub- space. In this work, we argue that both sparsity and the grouping effect are important for subspace segmentation. A sparse affinity matrix tends to be block diagonal, with less connections between data points from different sub- spaces. The grouping effect ensures that the highly cor- rected data which are usually from the same subspace can be grouped together. Sparse Subspace Clustering (SSC), by using l1-minimization, encourages sparsity for data se- lection, but it lacks of the grouping effect. On the contrary, Low-Rank Representation (LRR), by rank minimization, and Least Squares Regression (LSR), by l2-regularization, ex- hibit strong grouping effect, but they are short in subset s- election. Thus the obtained affinity matrix is usually very sparse by SSC, yet very dense by LRR and LSR.
In this work, we propose the Correlation Adaptive Sub- space Segmentation (CASS) method by using trace Lasso. CASS is a data correlation dependent method which simul- taneously performs automatic data selection and groups correlated data together. It can be regarded as a method which adap
|
Similar papers:
[rank all papers by similarity to this]
|
|
Correntropy Induced L2 Graph for Robust Subspace Clustering [pdf]
Canyi Lu, Jinhui Tang, Min Lin, Liang Lin, Shuicheng Yan, Zhouchen Lin |
|
Abstract: In this paper, we study the robust subspace clustering problem, which aims to cluster the given possibly noisy da- ta points into their underlying subspaces. A large pool of previous subspace clustering methods focus on the graph construction by different regularization of the representa- tion coefficient. We instead focus on the robustness of the model to non-Gaussian noises. We propose a new robust clustering method by using the correntropy induced metric, which is robust for handling the non-Gaussian and impul- sive noises. Also we further extend the method for handling the data with outlier rows/features. The multiplicative form of half-quadratic optimization is used to optimize the non- convex correntropy objective function of the proposed mod- els. Extensive experiments on face datasets well demon- strate that the proposed methods are more robust to corrup- tions and occlusions.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: When face images are taken in the wild, the large varia- tions in facial pose, illumination, and expression make face recognition challenging. The most fundamental problem for face recognition is to measure the similarity between faces. The traditional measurements such as various mathematical norms, Hausdorff distance, and approximate geodesic distance cannot accurately capture the structural information between faces in such complex circumstances. To address this issue, we develop a novel face patch network, based on which we define a new similarity measure called the random path (RP) measure. The RP measure is derived from the collective similarity of paths by performing random walks in the network. It can globally characterize the contextual and curved structures of the face space. To apply the RP measure, we construct two kinds of networks: the in-face network and the out-face network. The in-face network is drawn from any two face images and captures the local structural information. The out-face network is constructed from all the training face patches, thereby modeling the global structures of face space. The two face networks are structurally complementary and can be combined together to improve the recognition performance. Experiments on the Multi-PIE and LFW benchmarks show that the RP measure outperforms most of the state-of-art algorithms for face recognition.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Image Set Classification Using Holistic Multiple Order Statistics Features and Localized Multi-kernel Metric Learning [pdf]
Jiwen Lu, Gang Wang, Pierre Moulin |
|
Abstract: This paper presents a new approach for image set classi- fication, where each training and testing example contains a set of image instances of an object captured from varying viewpoints or under varying illuminations. While a number of image set classification methods have been proposed in recent years, most of them model each image set as a single linear subspace or mixture of linear subspaces, which may lose some discriminative information for classification. To address this, we propose exploring multiple order statistics as features of image sets, and develop a localized multi- kernel metric learning (LMKML) algorithm to effectively combine different order statistics information for classifica- tion. Our method achieves the state-of-the-art performance on four widely used databases including the Honda/UCSD, CMU Mobo, and Youtube face datasets, and the ETH-80 object dataset.
|
Similar papers:
[rank all papers by similarity to this]
|
|
A Deep Sum-Product Architecture for Robust Facial Attributes Analysis [pdf]
Ping Luo, Xiaogang Wang, Xiaoou Tang |
|
Abstract: Recent works have shown that facial attributes are useful in a number of applications such as face recognition and retrieval. However, estimating attributes in images with large variations remains a big challenge. This challenge is addressed in this paper. Unlike existing methods that assume the independence of attributes during their esti- mation, our approach captures the interdependencies of local regions for each attribute, as well as the high-order correlations between different attributes, which makes it more robust to occlusions and misdetection of face regions. First, we have modeled region interdependencies with a discriminative decision tree, where each node consists of a detector and a classifier trained on a local region. The detector allows us to locate the region, while the classifier determines the presence or absence of an attribute. Sec- ond, correlations of attributes and attribute predictors are modeled by organizing all of the decision trees into a large sum-product network (SPN), which is learned by the EM algorithm and yields the most probable explanation (MPE) of the facial attributes in terms of the regions localization and classification. Experimental results on a large data set with 22, 400 images show the effectiveness of the proposed approach.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Group Sparsity and Geometry Constrained Dictionary Learning for Action Recognition from Depth Maps [pdf]
Jiajia Luo, Wei Wang, Hairong Qi |
|
Abstract: Human action recognition based on the depth informa- tion provided by commodity depth sensors is an impor- tant yet challenging task. The noisy depth maps, differ- ent lengths of action sequences, and free styles in per- forming actions, may cause large intra-class variations. In this paper, a new framework based on sparse coding and temporal pyramid matching (TPM) is proposed for depth- based human action recognition. Especially, a discrimina- tive class-specific dictionary learning algorithm is proposed for sparse coding. By adding the group sparsity and geom- etry constraints, features can be well reconstructed by the sub-dictionary belonging to the same class, and the geom- etry relationships among features are also kept in the cal- culated coefficients. The proposed approach is evaluated on two benchmark datasets captured by depth cameras. Exper- imental results show that the proposed algorithm repeatedly achieves superior performance to the state of the art algo- rithms. Moreover, the proposed dictionary learning method also outperforms classic dictionary learning approaches.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: We propose a new Deep Decompositional Network (DDN) for parsing pedestrian images into semantic regions, such as hair, head, body, arms, and legs, where the pedestri- ans can be heavily occluded. Unlike existing methods based on template matching or Bayesian inference, our approach directly maps low-level visual features to the label maps of body parts with DDN, which is able to accurately estimate complex pose variations with good robustness to occlusions and background clutters. DDN jointly estimates occluded regions and segments body parts by stacking three types of hidden layers: occlusion estimation layers, completion layers, and decomposition layers. The occlusion estimation layers estimate a binary mask, indicating which part of a pedestrian is invisible. The completion layers synthesize low-level features of the invisible part from the original features and the occlusion mask. The decomposition layers directly transform the synthesized visual features to label maps. We devise a new strategy to pre-train these hidden layers, and then fine-tune the entire network using the stochastic gradient descent. Experimental results show that our approach achieves better segmentation accuracy than the state-of-the-art methods on pedestrian images with or without occlusions. Another important contribution of this paper is that it provides a large scale benchmark human parsing dataset1 that includes 3,673 annotated samples collected from 171 surveillance videos. It is 20 times large
|
Similar papers:
[rank all papers by similarity to this]
|
|
A Method of Perceptual-Based Shape Decomposition [pdf]
Chang Ma, Zhongqian Dong, Tingting Jiang, Yizhou Wang, Wen Gao |
|
Abstract: In this paper, we propose a novel perception-based shape decomposition method which aims to decompose a shape into semantically meaningful parts. In addition to three popular perception rules (the Minima rule, the Short-cut rule and the Convexity rule) in shape decomposition, we propose a new rule named part-similarity rule to encourage consistent partition of similar parts. The problem is for- mulated as a quadratically constrained quadratic program (QCQP) problem and is solved by a trust-region method. Experiment results on MPEG-7 dataset show that we can get a more consistent shape decomposition with human per- ception compared with other state-of-the-art methods both qualitatively and quantitatively. Finally, we show the ad- vantage of semantic parts over non-meaningful parts in ob- ject detection on the ETHZ dataset.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Action Recognition and Localization by Hierarchical Space-Time Segments [pdf]
Shugao Ma, Jianming Zhang, Nazli Ikizler-Cinbis, Stan Sclaroff |
|
Abstract: We propose Hierarchical Space-Time Segments as a new representation for action recognition and localization. This representation has a two-level hierarchy. The first level comprises the root space-time segments that may contain a human body. The second level comprises multi-grained space-time segments that contain parts of the root. We present an unsupervised method to generate this represen- tation from video, which extracts both static and non-static relevant space-time segments, and also preserves their hi- erarchical and temporal relationships. Using simple linear SVM on the resultant bag of hierarchical space-time seg- ments representation, we attain better than, or comparable to, state-of-the-art action recognition performance on two challenging benchmark datasets and at the same time pro- duce good action localization results.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Constant Time Weighted Median Filtering for Stereo Matching and Beyond [pdf]
Ziyang Ma, Kaiming He, Yichen Wei, Jian Sun, Enhua Wu |
|
Abstract: Despite the continuous advances in local stereo match- ing for years, most efforts are on developing robust cost computation and aggregation methods. Little attention has been seriously paid to the disparity refinement.
In this work, we study weighted median filtering for dis- parity refinement. We discover that with this refinement, even the simple box filter aggregation achieves comparable accuracy with various sophisticated aggregation methods (with the same refinement). This is due to the nice weighted median filtering properties of removing outlier error while respecting edges/structures. This reveals that the previously overlooked refinement can be at least as crucial as aggre- gation. We also develop the first constant time algorithm for the previously time-consuming weighted median filter. This makes the simple combination box aggregation + weight- ed median an attractive solution in practice for both speed and accuracy.
As a byproduct, the fast weighted median filtering un- leashes its potential in other applications that were ham- pered by high complexities. We show its superiority in var- ious applications such as depth upsampling, clip-art JPEG artifact removal, and image stylization.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Domain Transfer Support Vector Ranking for Person Re-identification without Target Camera Label Information [pdf]
Andy J. Ma, Pong C. Yuen, Jiawei Li |
|
Abstract: This paper addresses a new person re-identification problem without the label information of persons under non-overlapping target cameras. Given the matched (posi- tive) and unmatched (negative) image pairs from source do- main cameras, as well as unmatched (negative) image pairs which can be easily generated from target domain cameras, we propose a Domain Transfer Ranked Support Vector Ma- chines (DTRSVM) method for re-identification under target domain cameras. To overcome the problems introduced due to the absence of matched (positive) image pairs in target domain, we relax the discriminative constraint to a nec- essary condition only relying on the positive mean in tar- get domain. By estimating the target positive mean using source and target domain data, a new discriminative model with high confidence in target positive mean and low con- fidence in target negative image pairs is developed. Since the necessary condition may not truly preserve the discrim- inability, multi-task support vector ranking is proposed to incorporate the training data from source domain with label information. Experimental results show that the proposed DTRSVM outperforms existing methods without using label information in target cameras. And the top 30 rank accura- cy can be improved by the proposed method upto 9.40% on publicly available person re-identification datasets.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Latent Multitask Learning for View-Invariant Action Recognition [pdf]
Behrooz Mahasseni, Sinisa Todorovic |
|
Abstract: This paper presents an approach to view-invariant ac- tion recognition, where human poses and motions exhibit large variations across different camera viewpoints. When each viewpoint of a given set of action classes is specified as a learning task then multitask learning appears suitable for achieving view invariance in recognition. We extend the standard multitask learning to allow identifying: (1) latent groupings of action views (i.e., tasks), and (2) discrimi- native action parts, along with joint learning of all tasks. This is because it seems reasonable to expect that certain distinct views are more correlated than some others, and thus identifying correlated views could improve recognition. Also, part-based modeling is expected to improve robust- ness against self-occlusion when actors are imaged from different views. Results on the benchmark datasets show that we outperform standard multitask learning by 21.9%, and the state-of-the-art alternatives by 4.56%.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Progressive Multigrid Eigensolvers for Multiscale Spectral Segmentation [pdf]
Michael Maire, Stella X. Yu |
|
Abstract: We reexamine the role of multiscale cues in image seg- mentation using an architecture that constructs a globally coherent scale-space output representation. This charac- teristic is in contrast to many existing works on bottom-up segmentation, which prematurely compress information into a single scale. The architecture is a standard extension of Normalized Cuts from an image plane to an image pyramid, with cross-scale constraints enforcing consistency in the so- lution while allowing emergence of coarse-to-fine detail.
We observe that multiscale processing, in addition to im- proving segmentation quality, offers a route by which to speed computation. We make a significant algorithmic ad- vance in the form of a custom multigrid eigensolver for con- strained Angular Embedding problems possessing coarse- to-fine structure. Multiscale Normalized Cuts is a special case. Our solver builds atop recent results on randomized matrix approximation, using a novel interpolation opera- tion to mold its computational strategy according to cross- scale constraints in the problem definition. Applying our solver to multiscale segmentation problems demonstrates speedup by more than an order of magnitude. This speedup is at the algorithmic level and carries over to any imple- mentation target.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Prime Object Proposals with Randomized Prim's Algorithm [pdf]
Santiago Manen, Matthieu Guillaumin, Luc Van_Gool |
|
Abstract: Generic object detection is the challenging task of proposing windows that localize all the objects in an image, regardless of their classes. Such detectors have recently been shown to benefit many applications such as speeding- up class-specific object detection, weakly supervised learn- ing of object detectors and object discovery.
In this paper, we introduce a novel and very efficient method for generic object detection based on a randomized version of Prims algorithm. Using the connectivity graph of an images superpixels, with weights modelling the prob- ability that neighbouring superpixels belong to the same ob- ject, the algorithm generates random partial spanning trees with large expected sum of edge weights. Object localiza- tions are proposed as bounding-boxes of those partial trees.
Our method has several benefits compared to the state- of-the-art. Thanks to the efficiency of Prims algorithm, it samples proposals very quickly: 1000 proposals are ob- tained in about 0.7s. With proposals bound to superpixel boundaries yet diversified by randomization, it yields very high detection rates and windows that tightly fit objects.
In extensive experiments on the challenging PASCAL VOC 2007 and 2012 and SUN2012 benchmark datasets, we show that our method improves over state-of-the-art com- petitors for a wide range of evaluation scenarios.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Random Forests of Local Experts for Pedestrian Detection [pdf]
Javier Marin, David Vazquez, Antonio M. Lopez, Jaume Amores, Bastian Leibe |
|
Abstract: Pedestrian detection is one of the most challenging tasks in computer vision, and has received a lot of attention in the last years. Recently, some authors have shown the advan- tages of using combinations of part/patch-based detectors in order to cope with the large variability of poses and the existence of partial occlusions. In this paper, we propose a pedestrian detection method that efficiently combines mul- tiple local experts by means of a Random Forest ensemble. The proposed method works with rich block-based repre- sentations such as HOG and LBP, in such a way that the same features are reused by the multiple local experts, so that no extra computational cost is needed with respect to a holistic method. Furthermore, we demonstrate how to inte- grate the proposed approach with a cascaded architecture in order to achieve not only high accuracy but also an ac- ceptable efficiency. In particular, the resulting detector op- erates at five frames per second using a laptop machine. We tested the proposed method with well-known challeng- ing datasets such as Caltech, ETH, Daimler, and INRIA. The method proposed in this work consistently ranks among the top performers in all the datasets, being either the best method or having a small difference with the best one.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Pictorial Human Spaces: How Well Do Humans Perceive a 3D Articulated Pose? [pdf]
Elisabeta Marinoiu, Dragos Papava, Cristian Sminchisescu |
|
Abstract: Human motion analysis in images and video is a central computer vision problem. Yet, there are no studies that re- veal how humans perceive other people in images and how accurate they are. In this paper we aim to unveil some of the processingas well as the levels of accuracyinvolved in the 3D perception of people from images by assessing the hu- man performance. Our contributions are: (1) the construc- tion of an experimental apparatus that relates perception and measurement, in particular the visual and kinematic performance with respect to 3D ground truth when the hu- man subject is presented an image of a person in a given pose; (2) the creation of a dataset containing images, ar- ticulated 2D and 3D pose ground truth, as well as synchro- nized eye movement recordings of human subjects, shown a variety of human body configurations, both easy and diffi- cult, as well as their re-enacted 3D poses; (3) quantitative analysis revealing the human performance in 3D pose re- enactment tasks, the degree of stability in the visual fixation patterns of human subjects, and the way it correlates with different poses. We also discuss the implications of our find- ings for the construction of visual human sensing systems.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Subpixel Scanning Invariant to Indirect Lighting Using Quadratic Code Length [pdf]
Nicolas Martin, Vincent Couture, Sebastien Roy |
|
Abstract: We present a scanning method that recovers dense sub- pixel camera-projector correspondence without requiring any photometric calibration nor preliminary knowledge of their relative geometry. Subpixel accuracy is achieved by considering several zero-crossings defined by the difference between pairs of unstructured patterns. We use gray-level band-pass white noise patterns that increase robustness to indirect lighting and scene discontinuities. Simulated and experimental results show that our method recovers scene geometry with high subpixel precision, and that it can han- dle many challenges of active reconstruction systems. We compare our results to state of the art methods such as mi- cro phase shifting and modulated phase shifting.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Handling Occlusions with Franken-Classifiers [pdf]
Markus Mathias, Rodrigo Benenson, Radu Timofte, Luc Van_Gool |
|
Abstract: Detecting partially occluded pedestrians is challenging. A common practice to maximize detection quality is to train a set of occlusion-specific classifiers, each for a certain amount and type of occlusion. Since training classifiers is expensive, only a handful are typically trained. We show that by using many occlusion-specific classifiers, we outper- form previous approaches on three pedestrian datasets; IN- RIA, ETH, and Caltech USA. We present a new approach to train such classifiers. By reusing computations among dif- ferent training stages, 16 occlusion-specific classifiers can be trained at only one tenth the cost of one full training. We show that also test time cost grows sub-linearly.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: Geometry and geography can play an important role in recognition tasks in computer vision. To aid in study- ing connections between geometry and recognition, we in- troduce NYC3DCars, a rich dataset for vehicle detection in urban scenes built from Internet photos drawn from the wild, focused on densely trafficked areas of New York City. Our dataset is augmented with detailed geometric and ge- ographic information, including full camera poses derived from structure from motion, 3D vehicle annotations, and geographic information from open resources, including road segmentations and directions of travel. NYC3DCars can be used to study new questions about using geometric in- formation in detection tasks, and to explore applications of Internet photos in understanding cities. To demonstrate the utility of our data, we evaluate the use of the geographic in- formation in our dataset to enhance a parts-based detection method, and suggest other avenues for future exploration.
|
Similar papers:
[rank all papers by similarity to this]
|
|
A Unified Rolling Shutter and Motion Blur Model for 3D Visual Registration [pdf]
Maxime Meilland, Tom Drummond, Andrew I. Comport |
|
Abstract: Motion blur and rolling shutter deformations both inhibit visual motion registration, whether it be due to a moving sensor or a moving target. Whilst both deformations ex- ist simultaneously, no models have been proposed to han- dle them together. Furthermore, neither deformation has been considered previously in the context of monocular full- image 6 degrees of freedom registration or RGB-D structure and motion. As will be shown, rolling shutter deformation is observed when a camera moves faster than a single pixel in parallax between subsequent scan-lines. Blur is a function of the pixel exposure time and the motion vector. In this pa- per a complete dense 3D registration model will be derived to account for both motion blur and rolling shutter deforma- tions simultaneously. Various approaches will be compared with respect to ground truth and live real-time performance will be demonstrated for complex scenarios where both blur and shutter deformations are dominant.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Unsupervised Intrinsic Calibration from a Single Frame Using a "Plumb-Line" Approach [pdf]
R. Melo, M. Antunes, J.P. Barreto, G. Falcao, N. Goncalves |
|
Abstract: Estimating the amount and center of distortion from lines in the scene has been addressed in the literature by the so- called plumb-line approach. In this paper we propose a new geometric method to estimate not only the distortion parameters but the entire camera calibration (up to an an- gular scale factor) using a minimum of 3 lines. We pro- pose a new framework for the unsupervised simultaneous detection of natural image of lines and camera parameters estimation, enabling a robust calibration from a single im- age. Comparative experiments with existing automatic ap- proaches for the distortion estimation and with ground truth data are presented.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Efficient Image Dehazing with Boundary Constraint and Contextual Regularization [pdf]
Gaofeng Meng, Ying Wang, Jiangyong Duan, Shiming Xiang, Chunhong Pan |
|
Abstract: Images captured in foggy weather conditions often suffer from bad visibility. In this paper, we propose an efficient regularization method to remove hazes from a single input image. Our method benefits much from an exploration on the inherent boundary constraint on the transmission function. This constraint, combined with a weighted L1norm based contextual regularization, is modeled into an optimization problem to estimate the unknown scene transmission. A quite efficient algorithm based on variable splitting is also presented to solve the problem. The proposed method requires only a few general assumptions and can restore a high-quality haze-free image with faithful colors and fine image details. Experimental results on a variety of haze images demonstrate the effectiveness and efficiency of the proposed method.
Keywords-image processing; single image dehazing; visibility enhancement;
I. INTRODUCTION
When one takes a picture in foggy weather conditions, the obtained image often suffers from poor visibility. The distant objects in the fog lose the contrasts and get blurred with their surroundings, as illustrated in Figure 1. This is because the reflected light from these objects, before it reaches the camera, is attenuated in the air and further blended with the atmospheric light scattered by some aerosols (e.g., dust and water-droplets). Also for this reason, the colors of these objects get faded and become much similar to the fog, the similarity of which depending on the d
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: Many problems in computer vision can be posed as recovering a low-dimensional subspace from high- dimensional visual data. Factorization approaches to low- rank subspace estimation minimize a loss function between an observed measurement matrix and a bilinear factoriza- tion. Most popular loss functions include the L2 and L1 losses. L2 is optimal for Gaussian noise, while L1 is for Laplacian distributed noise. However, real data is often corrupted by an unknown noise distribution, which is un- likely to be purely Gaussian or Laplacian. To address this problem, this paper proposes a low-rank matrix factoriza- tion problem with a Mixture of Gaussians (MoG) noise model. The MoG model is a universal approximator for any continuous distribution, and hence is able to model a wider range of noise distributions. The parameters of the MoG model can be estimated with a maximum likelihood method, while the subspace is computed with standard approaches. We illustrate the benefits of our approach in extensive syn- thetic and real-world experiments including structure from motion, face modeling and background subtraction.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: Super resolution (SR) algorithms typically assume that the blur kernel is known (either the Point Spread Function PSF of the camera, or some default low-pass filter, e.g. a Gaussian). However, the performance of SR methods sig- nificantly deteriorates when the assumed blur kernel devi- ates from the true one. We propose a general framework for blind super resolution. In particular, we show that: (i) Unlike the common belief, the PSF of the camera is the wrong blur kernel to use in SR algorithms. (ii) We show how the correct SR blur kernel can be recovered directly from the low-resolution image. This is done by exploiting the in- herent recurrence property of small natural image patches (either internally within the same image, or externally in a collection of other natural images). In particular, we show that recurrence of small patches across scales of the low-res image (which forms the basis for single-image SR), can also be used for estimating the optimal blur kernel. This leads to significant improvement in SR results.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: We propose an unsupervised domain adaptation method that exploits intrinsic compact structures of categories across different domains using binary attributes. Our method directly optimizes for classification in the target do- main. The key insight is finding attributes that are discrim- inative across categories and predictable across domains. We achieve a performance that significantly exceeds the state-of-the-art results on standard benchmarks. In fact, in many cases, our method reaches the same-domain perfor- mance, the upper bound, in unsupervised domain adapta- tion scenarios.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: We present an approach for the text-to-image retrieval problem based on textual content present in images. Given the recent developments in understanding text in images, an appealing approach to address this problem is to localize and recognize the text, and then query the database, as in a text retrieval problem. We show that such an approach, de- spite being based on state-of-the-art methods, is insufficient, and propose a method, where we do not rely on an exact lo- calization and recognition pipeline. We take a query-driven search approach, where we find approximate locations of characters in the text query, and then impose spatial con- straints to generate a ranked list of images in the database. The retrieval performance is evaluated on public scene text datasets as well as three large datasets, namely IIIT scene text retrieval, Sports-10K and TV series-1M, we introduce.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Global Fusion of Relative Motions for Robust, Accurate and Scalable Structure from Motion [pdf]
Pierre Moulon, Pascal Monasse, Renaud Marlet |
|
Abstract: Multi-view structure from motion (SfM) estimates the po- sition and orientation of pictures in a common 3D coordi- nate frame. When views are treated incrementally, this ex- ternal calibration can be subject to drift, contrary to global methods that distribute residual errors evenly. We propose a new global calibration approach based on the fusion of rel- ative motions between image pairs. We improve an existing method for robustly computing global rotations. We present an efficient a contrario trifocal tensor estimation method, from which stable and precise translation directions can be extracted. We also define an efficient translation registra- tion method that recovers accurate camera positions. These components are combined into an original SfM pipeline. Our experiments show that, on most datasets, it outperforms in accuracy other existing incremental and global pipelines. It also achieves strikingly good running times: it is about 20 times faster than the other global method we could compare to, and as fast as the best incremental method. More impor- tantly, it features better scalability properties.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: Inference in continuous label Markov random fields is a challenging task. We use particle belief propagation (PBP) for solving the inference problem in continuous la- bel space. Sampling particles from the belief distribution is typically done by using Metropolis-Hastings (MH) Markov chain Monte Carlo (MCMC) methods which involves sam- pling from a proposal distribution. This proposal distribu- tion has to be carefully designed depending on the partic- ular model and input data to achieve fast convergence. We propose to avoid dependence on a proposal distribution by introducing a slice sampling based PBP algorithm. The pro- posed approach shows superior convergence performance on an image denoising toy example. Our findings are val- idated on a challenging relational 2D feature tracking ap- plication.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Coherent Motion Segmentation in Moving Camera Videos Using Optical Flow Orientations [pdf]
Manjunath Narayana, Allen Hanson, Erik Learned-Miller |
|
Abstract: In moving camera videos, motion segmentation is com- monly performed using the image plane motion of pixels, or optical flow. However, objects that are at different depths from the camera can exhibit different optical flows even if they share the same real-world motion. This can cause a depth-dependent segmentation of the scene. Our goal is to develop a segmentation algorithm that clusters pixels that have similar real-world motion irrespective of their depth in the scene. Our solution uses optical flow orientations in- stead of the complete vectors and exploits the well-known property that under camera translation, optical flow ori- entations are independent of object depth. We introduce a probabilistic model that automatically estimates the number of observed independent motions and results in a labeling that is consistent with real-world motion in the scene. The result of our system is that static objects are correctly iden- tified as one segment, even if they are at different depths. Color features and information from previous frames in the video sequence are used to correct occasional errors due to the orientation-based segmentation. We present results on more than thirty videos from different benchmarks. The system is particularly robust on complex background scenes containing objects at significantly different depths.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Scene Text Localization and Recognition with Oriented Stroke Detection [pdf]
Lukas Neumann, Jiri Matas |
|
Abstract: An unconstrained end-to-end text localization and recog- nition method is presented. The method introduces a novel approach for character detection and recognition which combines the advantages of sliding-window and connected component methods. Characters are detected and recog- nized as image regions which contain strokes of specific ori- entations in a specific relative position, where the strokes are efficiently detected by convolving the image gradient field with a set of oriented bar filters.
Additionally, a novel character representation efficiently calculated from the values obtained in the stroke detection phase is introduced. The representation is robust to shift at the stroke level, which makes it less sensitive to intra-class variations and the noise induced by normalizing character size and positioning. The effectiveness of the representa- tion is demonstrated by the results achieved in the classifi- cation of real-world characters using an euclidian nearest- neighbor classifier trained on synthetic data in a plain form. The method was evaluated on a standard dataset, where it achieves state-of-the-art results in both text localization and recognition.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: We aim to unsupervisedly discover humans action (mo- tion) patterns of manipulating various objects in scenarios such as assisted living. We are motivated by two key ob- servations. First, large variation exists in motion patterns associated with various types of objects being manipulated, thus manually defining motion primitives is infeasible. Sec- ond, some motion patterns are shared among different ob- jects being manipulated while others are object specific. We therefore propose a nonparametric Bayesian method that adopts a hierarchical Dirichlet process prior to learn rep- resentative manipulation (motion) patterns in an unsuper- vised manner. Taking easy-to-obtain object detection score maps and dense motion trajectories as inputs, the proposed probabilistic model can discover motion pattern groups as- sociated with different types of objects being manipulated with a shared manipulation pattern dictionary. The size of the learned dictionary is automatically inferred. Com- prehensive experiments on two assisted living benchmarks and a cooking motion dataset demonstrate superiority of our learned manipulation pattern dictionary in represent- ing manipulation actions for recognition.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Proportion Priors for Image Sequence Segmentation [pdf]
Claudia Nieuwenhuis, Evgeny Strekalovskiy, Daniel Cremers |
|
Abstract: We propose a convex multilabel framework for image sequence segmentation which allows to impose proportion priors on object parts in order to preserve their size ratios across multiple images. The key idea is that for strongly de- formable objects such as a gymnast the size ratio of respec- tive regions (head versus torso, legs versus full body, etc.) is typically preserved. We propose different ways to impose such priors in a Bayesian framework for image segmenta- tion. We show that near-optimal solutions can be computed using convex relaxation techniques. Extensive qualitative and quantitative evaluations demonstrate that the propor- tion priors allow for highly accurate segmentations, avoid- ing seeping-out of regions and preserving semantically rel- evant small-scale structures such as hands or feet. They naturally apply to multiple object instances such as play- ers in sports scenes, and they can relate different objects instead of object parts, e.g. organs in medical imaging. The algorithm is efficient and easily parallelized leading to proportion-consistent segmentations at runtimes around one second.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Bounded Labeling Function for Global Segmentation of Multi-part Objects with Geometric Constraints [pdf]
Masoud S. Nosrati, Shawn Andrews, Ghassan Hamarneh |
|
Abstract: The inclusion of shape and appearance priors have proven useful for obtaining more accurate and plausible segmentations, especially for complex objects with multi- ple parts. In this paper, we augment the popular Mumford- Shah model to incorporate two important geometrical con- straints, termed containment and detachment, between dif- ferent regions with a specified minimum distance between their boundaries. Our method is able to handle multiple in- stances of multi-part objects defined by these geometrical constraints using a single labeling function while maintain- ing global optimality. We demonstrate the utility and advan- tages of these two constraints and show that the proposed convex continuous method is superior to other state-of-the- art methods, including its discrete counterpart, in terms of memory usage, and metrication errors.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Partial Sum Minimization of Singular Values in RPCA for Low-Level Vision [pdf]
Tae-Hyun Oh, Hyeongwoo Kim, Yu-Wing Tai, Jean-Charles Bazin, In So Kweon |
|
Abstract: Robust Principal Component Analysis (RPCA) via rank minimization is a powerful tool for recovering underly- ing low-rank structure of clean data corrupted with sparse noise/outliers. In many low-level vision problems, not only it is known that the underlying structure of clean data is low-rank, but the exact rank of clean data is also known. Yet, when applying conventional rank minimization for those problems, the objective function is formulated in a way that does not fully utilize a priori target rank infor- mation about the problems. This observation motivates us to investigate whether there is a better alternative solution when using rank minimization.
In this paper, instead of minimizing the nuclear norm, we propose to minimize the partial sum of singular values. The proposed objective function implicitly encourages the tar- get rank constraint in rank minimization. Our experimen- tal analyses show that our approach performs better than conventional rank minimization when the number of sam- ples is deficient, while the solutions obtained by the two ap- proaches are almost identical when the number of samples is more than sufficient. We apply our approach to various low-level vision problems, e.g. high dynamic range imag- ing, photometric stereo and image alignment, and show that our results outperform those obtained by the conventional nuclear norm rank minimization method.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Partial Enumeration and Curvature Regularization [pdf]
Carl Olsson, Johannes Ulen, Yuri Boykov, Vladimir Kolmogorov |
|
Abstract: Energies with high-order non-submodular interactions have been shown to be very useful in vision due to their high modeling power. Optimization of such energies, however, is generally NP-hard. A naive approach that works for small problem instances is exhaustive search, that is, enumera- tion of all possible labelings of the underlying graph. We propose a general minimization approach for large graphs based on enumeration of labelings of certain small patches. This partial enumeration technique reduces complex high- order energy formulations to pairwise Constraint Satisfac- tion Problems with unary costs (uCSP), which can be ef- ficiently solved using standard methods like TRW-S. Our approach outperforms a number of existing state-of-the-art algorithms on well known difficult problems (e.g. curvature regularization, stereo, deconvolution); it gives near global minimum and better speed.
Our main application of interest is curvature regular- ization. In the context of segmentation, our partial enu- meration technique allows to evaluate curvature directly on small patches using a novel integral geometry approach. 1
|
Similar papers:
[rank all papers by similarity to this]
|
|
Action and Event Recognition with Fisher Vectors on a Compact Feature Set [pdf]
Dan Oneata, Jakob Verbeek, Cordelia Schmid |
|
Abstract: Action recognition in uncontrolled video is an important and challenging computer vision problem. Recent progress in this area is due to new local features and models that capture spatio-temporal structure between local features, or human-object interactions. Instead of working towards more complex models, we focus on the low-level features and their encoding. We evaluate the use of Fisher vectors as an alternative to bag-of-word histograms to aggregate a small set of state-of-the-art low-level descriptors, in combi- nation with linear classifiers. We present a large and var- ied set of evaluations, considering (i) classification of short actions in five datasets, (ii) localization of such actions in feature-length movies, and (iii) large-scale recognition of complex events. We find that for basic action recognition and localization MBH features alone are enough for state- of-the-art performance. For complex events we find that SIFT and MFCC features provide complementary cues. On all three problems we obtain state-of-the-art results, while using fewer features and less complex models.
|
Similar papers:
[rank all papers by similarity to this]
|
|
From Large Scale Image Categorization to Entry-Level Categories [pdf]
Vicente Ordonez, Jia Deng, Yejin Choi, Alexander C. Berg, Tamara L. Berg |
|
Abstract: Entry level categories the labels people will use to name an object were originally defined and studied by psychologists in the 1980s. In this paper we study entry- level categories at a large scale and learn the first mod- els for predicting entry-level categories for images. Our models combine visual recognition predictions with proxies for word naturalness mined from the enormous amounts of text on the web. We demonstrate the usefulness of our models for predicting nouns (entry-level words) associated with images by people. We also learn mappings between concepts predicted by existing visual recognition systems and entry-level concepts that could be useful for improv- ing human-focused applications such as natural language image description or retrieval.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: Feature extraction, deformation handling, occlusion handling, and classication are four important components in pedestrian detection. Existing methods learn or design these components either individually or sequentially. The interaction among these components is not yet well ex- plored. This paper proposes that they should be jointly learned in order to maximize their strengths through coop- eration. We formulate these four components into a joint deep learning framework and propose a new deep network architecture1. By establishing automatic, mutual interac- tion among components, the deep model achieves a 9% re- duction in the average miss rate compared with the cur- rent best-performing pedestrian detection approaches on the largest Caltech benchmark dataset.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Shape Anchors for Data-Driven Multi-view Reconstruction [pdf]
Andrew Owens, Jianxiong Xiao, Antonio Torralba, William Freeman |
|
Abstract: We present a data-driven method for building dense 3D reconstructions using a combination of recognition and multi-view cues. Our approach is based on the idea that there are image patches that are so distinctive that we can accurately estimate their latent 3D shapes solely using recognition. We call these patches shape anchors, and we use them as the basis of a multi-view reconstruction system that transfers dense, complex geometry between scenes. We anchor our 3D interpretation from these patches, using them to predict geometry for parts of the scene that are rel- atively ambiguous. The resulting algorithm produces dense reconstructions from stereo point clouds that are sparse and noisy, and we demonstrate it on a challenging dataset of real-world, indoor scenes.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Efficient Pedestrian Detection by Directly Optimizing the Partial Area under the ROC Curve [pdf]
Sakrapee Paisitkriangkrai, Chunhua Shen, Anton Van Den Hengel |
|
Abstract: Many typical applications of object detection operate within a prescribed false-positive range. In this situation the performance of a detector should be assessed on the ba- sis of the area under the ROC curve over that range, rather than over the full curve, as the performance outside the range is irrelevant. This measure is labelled as the partial area under the ROC curve (pAUC). Effective cascade-based classification, for example, depends on training node classi- fiers that achieve the maximal detection rate at a moderate false positive rate, e.g., around 40% to 50%. We propose a novel ensemble learning method which achieves a maxi- mal detection rate at a user-defined range of false positive rates by directly optimizing the partial AUC using struc- tured learning. By optimizing for different ranges of false positive rates, the proposed method can be used to train ei- ther a single strong classifier or a node classifier forming part of a cascade classifier. Experimental results on both synthetic and real-world data sets demonstrate the effec- tiveness of our approach, and we show that it is possible to train state-of-the-art pedestrian detectors using the pro- posed structured ensemble learning method.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Coherent Object Detection with 3D Geometric Context from a Single Image [pdf]
Jiyan Pan, Takeo Kanade |
|
Abstract: Objects in a real world image cannot have arbitrary ap- pearance, sizes and locations due to geometric constraints in 3D space. Such a 3D geometric context plays an im- portant role in resolving visual ambiguities and achiev- ing coherent object detection. In this paper, we develop a RANSAC-CRF framework to detect objects that are geo- metrically coherent in the 3D world. Different from existing methods, we propose a novel generalized RANSAC algo- rithm to generate global 3D geometry hypotheses from local entities such that outlier suppression and noise reduction is achieved simultaneously. In addition, we evaluate those hy- potheses using a CRF which considers both the compatibil- ity of individual objects under global 3D geometric context and the compatibility between adjacent objects under local 3D geometric context. Experiment results show that our ap- proach compares favorably with the state of the art.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Offline Mobile Instance Retrieval with a Small Memory Footprint [pdf]
Jayaguru Panda, Michael S. Brown, C.V. Jawahar |
|
Abstract: Existing mobile image instance retrieval applications as- sume a network-based usage where image features are sent to a server to query an online visual database. In this scenario, there are no restrictions on the size of the vi- sual database. This paper, however, examines how to per- form this same task offline, where the entire visual index must reside on the mobile device itself within a small mem- ory footprint. Such solutions have applications on location recognition and product recognition. Mobile instance re- trieval requires a significant reduction in the visual index size. To achieve this, we describe a set of strategies that can reduce the visual index up to 60-80 compared to a standard instance retrieval implementation found on desk- tops or servers. While our proposed reduction steps affect the overall mean Average Precision (mAP), they are able to maintain a good Precision for the top K results (PK). We argue that for such offline application, maintaining a good PK is sufficient. The effectiveness of this approach is demonstrated on several standard databases. A working application designed for a remote historical site is also pre- sented. This application is able to reduce an 50,000 image index structure to 25 MBs while providing a precision of 97% for P10 and 100% for P1.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Finding the Best from the Second Bests - Inhibiting Subjective Bias in Evaluation of Visual Tracking Algorithms [pdf]
Yu Pang, Haibin Ling |
|
Abstract: Evaluating visual tracking algorithms, or trackers for short, is of great importance in computer vision. However, it is hard to fairly compare trackers due to many param- eters need to be tuned in the experimental configurations. On the other hand, when introducing a new tracker, a re- cent trend is to validate it by comparing it with several ex- isting ones. Such an evaluation may have subjective biases towards the new tracker which typically performs the best. This is mainly due to the difficulty to optimally tune all its competitors and sometimes the selected testing sequences. By contrast, little subjective bias exists towards the sec- ond best ones1 in the contest. This observation inspires us with a novel perspective towards inhibiting subjective bias in evaluating trackers by analyzing the results between the second bests. In particular, we first collect all tracking papers published in major computer vision venues in re- cent years. From these papers, after filtering out potential biases in various aspects, we create a dataset containing many records of comparison results between various visual trackers. Using these records, we derive performance rank- ings of the involved trackers by four different methods. The first two methods model the dataset as a graph and then derive the rankings over the graph, one by a rank aggrega- tion algorithm and the other by a PageRank-like solution. The other two methods take the records as generated from sports contests and adop
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: We present a technique for separating foreground objects from the background in a video. Our method is fast, fully au- tomatic, and makes minimal assumptions about the video. This enables handling essentially unconstrained settings, including rapidly moving background, arbitrary object mo- tion and appearance, and non-rigid deformations and ar- ticulations. In experiments on two datasets containing over 1400 video shots, our method outperforms a state-of-the- art background subtraction technique [4] as well as meth- ods based on clustering point tracks [6, 18, 19]. Moreover, it performs comparably to recent video object segmentation methods based on object proposals [14, 16, 27], while being orders of magnitude faster.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Implied Feedback: Learning Nuances of User Behavior in Image Search [pdf]
Devi Parikh, Kristen Grauman |
|
Abstract: User feedback helps an image search system refine its relevance predictions, tailoring the search towards the users preferences. Existing methods simply take feedback at face value: clicking on an image means the user wants things like it; commenting that an image lacks a specific attribute means the user wants things that have it. How- ever, we expect there is actually more information behind the users literal feedback. In particular, a users (possibly subconscious) search strategy leads him to comment on cer- tain images rather than others, based on how any of the vis- ible candidate images compare to the desired content. For example, he may be more likely to give negative feedback on an irrelevant image that is relatively close to his target, as opposed to bothering with one that is altogether different. We introduce novel features to capitalize on such implied feedback cues, and learn a ranking function that uses them to improve the systems relevance estimates. We validate the approach with real users searching for shoes, faces, or scenes using two different modes of feedback: binary rele- vance feedback and relative attributes-based feedback. The results show that retrieval improves significantly when the system accounts for the learned behaviors. We show that the nuances learned are domain-invariant, and useful for both generic user-independent search as well as personal- ized user-specific search.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Uncertainty-Driven Efficiently-Sampled Sparse Graphical Models for Concurrent Tumor Segmentation and Atlas Registration [pdf]
Sarah Parisot, William Wells_III, Stephane Chemouny, Hugues Duffau, Nikos Paragios |
|
Abstract: Graph-based methods have become popular in recent years and have successfully addressed tasks like segmen- tation and deformable registration. Their main strength is optimality of the obtained solution while their main limi- tation is the lack of precision due to the grid-like repre- sentations and the discrete nature of the quantized search space. In this paper we introduce a novel approach for com- bined segmentation/registration of brain tumors that adapts graph and sampling resolution according to the image con- tent. To this end we estimate the segmentation and registra- tion marginals towards adaptive graph resolution and in- telligent definition of the search space. This information is considered in a hierarchical framework where uncertain- ties are propagated in a natural manner. State of the art results in the joint segmentation/registration of brain im- ages with low-grade gliomas demonstrate the potential of our approach.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Multiview Photometric Stereo Using Planar Mesh Parameterization [pdf]
Jaesik Park, Sudipta N. Sinha, Yasuyuki Matsushita, Yu-Wing Tai, In So Kweon |
|
Abstract: We propose a method for accurate 3D shape reconstruc- tion using uncalibrated multiview photometric stereo. A coarse mesh reconstructed using multiview stereo is first parameterized using a planar mesh parameterization tech- nique. Subsequently, multiview photometric stereo is per- formed in the 2D parameter domain of the mesh, where all geometric and photometric cues from multiple images can be treated uniformly. Unlike traditional methods, there is no need for merging view-dependent surface normal maps. Our key contribution is a new photometric stereo based mesh refinement technique that can efficiently reconstruct meshes with extremely fine geometric details by directly estimating a displacement texture map in the 2D parame- ter domain. We demonstrate that intricate surface geome- try can be reconstructed using several challenging datasets containing surfaces with specular reflections, multiple albe- dos and complex topologies.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Predicting Primary Gaze Behavior Using Social Saliency Fields [pdf]
Hyun Soo Park, Eakta Jain, Yaser Sheikh |
|
Abstract: We present a method to predict primary gaze behavior in a social scene. Inspired by the study of electric fields, we posit social chargeslatent quantities that drive the primary gaze behavior of members of a social group. These charges induce a gradient field that defines the relationship between the social charges and the primary gaze direction of members in the scene. This field model is used to predict primary gaze behavior at any location or time in the scene. We present an algorithm to estimate the time-varying be- havior of these charges from the primary gaze behavior of measured observers in the scene. We validate the model by evaluating its predictive precision via cross-validation in a variety of social scenes.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: We propose a novel algorithm called Latent Space Sparse Subspace Clustering for simultaneous dimensional- ity reduction and clustering of data lying in a union of sub- spaces. Specifically, we describe a method that learns the projection of data and finds the sparse coefficients in the low-dimensional latent space. Cluster labels are then as- signed by applying spectral clustering to a similarity matrix built from these sparse coefficients. An efficient optimiza- tion method is proposed and its non-linear extensions based on the kernel methods are presented. One of the main ad- vantages of our method is that it is computationally efficient as the sparse coefficients are found in the low-dimensional latent space. Various experiments show that the proposed method performs better than the competitive state-of-the- art subspace clustering methods.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Shape Index Descriptors Applied to Texture-Based Galaxy Analysis [pdf]
Kim Steenstrup Pedersen, Kristoffer Stensbo-Smidt, Andrew Zirm, Christian Igel |
|
Abstract: A texture descriptor based on the shape index and the accompanying curvedness measure is proposed, and it is evaluated for the automated analysis of astronomical im- age data. A representative sample of images of low-redshift galaxies from the Sloan Digital Sky Survey (SDSS) serves as a testbed. The goal of applying texture descriptors to these data is to extract novel information about galaxies; information which is often lost in more traditional analy- sis. In this study, we build a regression model for predict- ing a spectroscopic quantity, the specific star-formation rate (sSFR). As texture features we consider multi-scale gradi- ent orientation histograms as well as multi-scale shape in- dex histograms, which lead to a new descriptor. Our re- sults show that we can successfully predict spectroscopic quantities from the texture in optical multi-band images. We successfully recover the observed bi-modal distribution of galaxies into quiescent and star-forming. The state-of- the-art for predicting the sSFR is a color-based physical model. We significantly improve its accuracy by augment- ing the model with texture information. This study is the first step towards enabling the quantification of physical galaxy properties from imaging data alone.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Unsupervised Random Forest Manifold Alignment for Lipreading [pdf]
Yuru Pei, Tae-Kyun Kim, Hongbin Zha |
|
Abstract: Lipreading from visual channels remains a challeng- ing topic considering the various speaking characteristics. In this paper, we address an efficient lipreading approach by investigating the unsupervised random forest manifold alignment (RFMA). The density random forest is employed to estimate affinity of patch trajectories in speaking facial videos. We propose novel criteria for node splitting to avoid the rank-deficiency in learning density forests. By virtue of the hierarchical structure of random forests, the trajecto- ry affinities are measured efficiently, which are used to find embeddings of the speaking video clips by a graph-based algorithm. Lipreading is formulated as matching between manifolds of query and reference video clips. We employ the manifold alignment technique for matching, where the L- norm-based manifold-to-manifold distance is proposed to find the matching pairs. We apply this random forest man- ifold alignment technique to various video data sets cap- tured by consumer cameras. The experiments demonstrate that lipreading can be performed effectively, and outperfor- m state-of-the-arts.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: Most sky models only describe the cloudiness of the over- all sky by a single category or parameter such as sky index, which does not account for the distribution of the cloud- s across the sky. To capture variable cloudiness, we extend the concept of sky index to a random field indicating the lev- el of cloudiness of each sky pixel in our proposed sky repre- sentation based on the Igawa sky model. We formulate the problem of solving the sky index of every sky pixel as a la- beling problem, where an approximate solution can be effi- ciently found. Experimental results show that our proposed sky model has better expressiveness, stability with respect to variation in camera parameters, and geo-location esti- mation in outdoor images compared to the uniform sky in- dex model. Potential applications of our proposed sky mod- el include sky image rendering, where sky images can be generated with an arbitrary cloud distribution at any time and any location, previously impossible with traditional sky models.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Recognizing Text with Perspective Distortion in Natural Scenes [pdf]
Trung Quy Phan, Palaiahnakote Shivakumara, Shangxuan Tian, Chew Lim Tan |
|
Abstract: This paper presents an approach to text recognition in natural scene images. Unlike most existing works which assume that texts are horizontal and frontal parallel to the image plane, our method is able to recognize perspective texts of arbitrary orientations. For individual character recognition, we adopt a bag-of-keypoints approach, in which Scale Invariant Feature Transform (SIFT) descriptors are extracted densely and quantized using a pre-trained vocabulary. Following [1, 2], the context information is utilized through lexicons. We formulate word recognition as finding the optimal alignment between the set of characters and the list of lexicon words. Furthermore, we introduce a new dataset called StreetViewText-Perspective, which contains texts in street images with a great variety of viewpoints. Experimental results on public datasets and the proposed dataset show that our method significantly outperforms the state-of-the-art on perspective texts of arbitrary orientations.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Strong Appearance and Expressive Spatial Models for Human Pose Estimation [pdf]
Leonid Pishchulin, Mykhaylo Andriluka, Peter Gehler, Bernt Schiele |
|
Abstract: Typical approaches to articulated pose estimation com- bine spatial modelling of the human body with appear- ance modelling of body parts. This paper aims to push the state-of-the-art in articulated pose estimation in two ways. First we explore various types of appearance representa- tions aiming to substantially improve the body part hypothe- ses. And second, we draw on and combine several recently proposed powerful ideas such as more flexible spatial mod- els as well as image-conditioned spatial models. In a se- ries of experiments we draw several important conclusions: (1) we show that the proposed appearance representations are complementary; (2) we demonstrate that even a basic tree-structure spatial human body model achieves state-of- the-art performance when augmented with the proper ap- pearance representation; and (3) we show that the com- bination of the best performing appearance model with a flexible image-conditioned spatial model achieves the best result, significantly improving over the state of the art, on the Leeds Sports Poses and Parse benchmarks.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Illuminant Chromaticity from Image Sequences [pdf]
Veronique Prinet, Dani Lischinski, Michael Werman |
|
Abstract: We estimate illuminant chromaticity from temporal se- quences, for scenes illuminated by either one or two dom- inant illuminants. While there are many methods for illu- minant estimation from a single image, few works so far have focused on videos, and even fewer on multiple light sources. Our aim is to leverage information provided by the temporal acquisition, where either the objects or the cam- era or the light source are/is in motion in order to estimate illuminant color without the need for user interaction or us- ing strong assumptions and heuristics. We introduce a sim- ple physically-based formulation based on the assumption that the incident light chromaticity is constant over a short space-time domain. We show that a deterministic approach is not sufficient for accurate and robust estimation: how- ever, a probabilistic formulation makes it possible to implic- itly integrate away hidden factors that have been ignored by the physical model. Experimental results are reported on a dataset of natural video sequences and on the GrayBall benchmark, indicating that we compare favorably with the state-of-the-art.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: This paper proposes modeling the complex web image collections with an automatically generated graph structure called visual semantic complex network (VSCN). The nodes on this complex network are clusters of images with both visual and semantic consistency, called semantic concepts. These nodes are connected based on the visual and seman- tic correlations. Our VSCN with 33, 240 concepts is gener- ated from a collection of 10 million web images. 1 A great deal of valuable information on the structures of the web image collections can be revealed by exploring the VSCN, such as the small-world behavior, concept community, in- degree distribution, hubs, and isolated concepts. It not only helps us better understand the web image collections at a macroscopic level, but also has many important practical applications. This paper presents two application exam- ples: content-based image retrieval and image browsing. Experimental results show that the VSCN leads to signifi- cant improvement on both the precision of image retrieval (over 200%) and user experience for image browsing.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Monocular Image 3D Human Pose Estimation under Self-Occlusion [pdf]
Ibrahim Radwan, Abhinav Dhall, Roland Goecke |
|
Abstract: In this paper, an automatic approach for 3D pose recon- struction from a single image is proposed. The presence of human body articulation, hallucinated parts and cluttered background leads to ambiguity during the pose inference, which makes the problem non-trivial. Researchers have ex- plored various methods based on motion and shading in or- der to reduce the ambiguity and reconstruct the 3D pose. The key idea of our algorithm is to impose both kinematic and orientation constraints. The former is imposed by pro- jecting a 3D model onto the input image and pruning the parts, which are incompatible with the anthropomorphism. The latter is applied by creating synthetic views via regress- ing the input view to multiple oriented views. After applying the constraints, the 3D model is projected onto the initial and synthetic views, which further reduces the ambiguity. Finally, we borrow the direction of the unambiguous parts from the synthetic views to the initial one, which results in the 3D pose. Quantitative experiments are performed on the HumanEva-I dataset and qualitatively on unconstrained images from the Image Parse dataset. The results show the robustness of the proposed approach to accurately recon- struct the 3D pose form a single image.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: We propose a novel and an efficient method for recon- structing the 3D arrangement of lines extracted from a sin- gle image, using vanishing points, orthogonal structure, and an optimization procedure that considers all plausible connectivity constraints between lines. Line detection iden- tifies a large number of salient lines that intersect or nearly intersect in an image, but relatively a few of these apparent junctions correspond to real intersections in the 3D scene. We use linear programming (LP) to identify a minimal set of least-violated connectivity constraints that are sufficient to unambiguously reconstruct the 3D lines. In contrast to prior solutions that primarily focused on well-behaved syn- thetic line drawings with severely restricting assumptions, we develop an algorithm that can work on real images. The algorithm produces line reconstruction by identifying 95% correct connectivity constraints in York Urban database, with a total computation time of 1 second per image.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Video Event Understanding Using Natural Language Descriptions [pdf]
Vignesh Ramanathan, Percy Liang, Li Fei-Fei |
|
Abstract: Human action and role recognition play an important part in complex event understanding. State-of-the-art meth- ods learn action and role models from detailed spatio tem- poral annotations, which requires extensive human effort. In this work, we propose a method to learn such mod- els based on natural language descriptions of the training videos, which are easier to collect and scale with the num- ber of actions and roles. There are two challenges with using this form of weak supervision: First, these descrip- tions only provide a high-level summary and often do not directly mention the actions and roles occurring in a video. Second, natural language descriptions do not provide spa- tio temporal annotations of actions and roles. To tackle these challenges, we introduce a topic-based semantic re- latedness (SR) measure between a video description and an action and role label, and incorporate it into a poste- rior regularization objective. Our event recognition system based on these action and role models matches the state-of- the-art method on the TRECVID-MED11 event kit, despite weaker supervision.
|
Similar papers:
[rank all papers by similarity to this]
|
|
STAR3D: Simultaneous Tracking and Reconstruction of 3D Objects Using RGB-D Data [pdf]
Carl Yuheng Ren, Victor Prisacariu, David Murray, Ian Reid |
|
Abstract: We introduce a probabilistic framework for simultane- ous tracking and reconstruction of 3D rigid objects using an RGB-D camera. The tracking problem is handled us- ing a bag-of-pixels representation and a back-projection scheme. Surface and background appearance models are learned online, leading to robust tracking in the presence of heavy occlusion and outliers. In both our tracking and re- construction modules, the 3D object is implicitly embedded using a 3D level-set function. The framework is initialized with a simple shape primitive model (e.g. a sphere or a cube), and the real 3D object shape is tracked and recon- structed online. Unlike existing depth-based 3D reconstruc- tion works, which either rely on calibrated/fixed camera set up or use the observed world map to track the depth camer- a, our framework can simultaneously track and reconstruct small moving objects. We use both qualitative and quan- titative results to demonstrate the superior performance of both tracking and reconstruction of our method.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Temporally Consistent Superpixels [pdf]
Matthias Reso, Jorn Jachalsky, Bodo Rosenhahn, Jorn Ostermann |
|
Abstract: Superpixel algorithms represent a very useful and in- creasingly popular preprocessing step for a wide range of computer vision applications, as they offer the potential to boost efficiency and effectiveness. In this regards, this paper presents a highly competitive approach for tempo- rally consistent superpixels for video content. The approach is based on energy-minimizing clustering utilizing a novel hybrid clustering strategy for a multi-dimensional feature space working in a global color subspace and local spatial subspaces. Moreover, a new contour evolution based strat- egy is introduced to ensure spatial coherency of the gener- ated superpixels. For a thorough evaluation the proposed approach is compared to state of the art supervoxel algo- rithms using established benchmarks and shows a superior performance.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: Dense motion of image points over many video frames can provide important information about the world. How- ever, occlusions and drift make it impossible to compute long motion paths by merely concatenating optical flow vec- tors between consecutive frames. Instead, we solve for en- tire paths directly, and flag the frames in which each is visible. As in previous work, we anchor each path to a unique pixel which guarantees an even spatial distribution of paths. Unlike earlier methods, we allow paths to be an- chored in any frame. By explicitly requiring that at least one visible path passes within a small neighborhood of every pixel, we guarantee complete coverage of all visible points in all frames. We achieve state-of-the-art results on real sequences including both rigid and non-rigid motions with significant occlusions.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Saliency and Human Fixations: State-of-the-Art and Study of Comparison Metrics [pdf]
Nicolas Riche, Matthieu Duvinage, Matei Mancas, Bernard Gosselin, Thierry Dutoit |
|
Abstract: Visual saliency has been an increasingly active research area in the last ten years with dozens of saliency models recently published. Nowadays, one of the big challenges in the field is to find a way to fairly evaluate all of these models. In this paper, on human eye fixations ,we compare the ranking of 12 state-of-the art saliency models using 12 similarity metrics. The comparison is done on Jian Lis database containing several hundreds of natural images. Based on Kendall concordance coefficient, it is shown that some of the metrics are strongly correlated leading to a re- dundancy in the performance metrics reported in the avail- able benchmarks. On the other hand, other metrics provide a more diverse picture of models overall performance. As a recommendation, three similarity metrics should be used to obtain a complete point of view of saliency model perfor- mance.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Discriminatively Trained Templates for 3D Object Detection: A Real Time Scalable Approach [pdf]
Reyes Rios-Cabrera, Tinne Tuytelaars |
|
Abstract: In this paper we propose a new method for detecting multiple specific 3D objects in real time. We start from the template-based approach based on the LINE2D/LINEMOD representation introduced recently by Hinterstoisser et al., yet extend it in two ways. First, we propose to learn the tem- plates in a discriminative fashion. We show that this can be done online during the collection of the example images, in just a few milliseconds, and has a big impact on the accu- racy of the detector. Second, we propose a scheme based on cascades that speeds up detection.
Since detection of an object is fast, new objects can be added with very low cost, making our approach scale well. In our experiments, we easily handle 10-30 3D objects at frame rates above 10fps using a single CPU core. We out- perform the state-of-the-art both in terms of speed as well as in terms of accuracy, as validated on 3 different datasets. This holds both when using monocular color images (with LINE2D) and when using RGBD images (with LINEMOD). Moreover, we propose a challenging new dataset made of 12 objects, for future competing methods on monocular color images.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Elastic Net Constraints for Shape Matching [pdf]
Emanuele Rodola, Andrea Torsello, Tatsuya Harada, Yasuo Kuniyoshi, Daniel Cremers |
|
Abstract: We consider a parametrized relaxation of the widely adopted quadratic assignment problem (QAP) formula- tion for minimum distortion correspondence between de- formable shapes. In order to control the accuracy/sparsity trade-off we introduce a weighting parameter on the com- bination of two existing relaxations, namely spectral and game-theoretic. This leads to the introduction of the elastic net penalty function into shape matching problems. In com- bination with an efficient algorithm to project onto the elas- tic net ball, we obtain an approach for deformable shape matching with controllable sparsity. Experiments on a stan- dard benchmark confirm the effectiveness of the approach.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Translating Video Content to Natural Language Descriptions [pdf]
Marcus Rohrbach, Wei Qiu, Ivan Titov, Stefan Thater, Manfred Pinkal, Bernt Schiele |
|
Abstract: Humans use rich natural language to describe and com- municate visual perceptions. In order to provide natural language descriptions for visual content, this paper com- bines two important ingredients. First, we generate a rich semantic representation of the visual content including e.g. object and activity labels. To predict the semantic represen- tation we learn a CRF to model the relationships between different components of the visual input. And second, we propose to formulate the generation of natural language as a machine translation problem using the semantic represen- tation as source language and the generated sentences as target language. For this we exploit the power of a parallel corpus of videos and textual descriptions and adapt statis- tical machine translation to translate between our two lan- guages. We evaluate our video descriptions on the TACoS dataset [23], which contains video snippets aligned with sentence descriptions. Using automatic evaluation and hu- man judgments we show significant improvements over sev- eral baseline approaches, motivated by prior work. Our translation approach also shows improvements over related work on an image description task.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Active MAP Inference in CRFs for Efficient Semantic Segmentation [pdf]
Gemma Roig, Xavier Boix, Roderick De_Nijs, Sebastian Ramos, Koljia Kuhnlenz, Luc Van_Gool |
|
Abstract: Most MAP inference algorithms for CRFs optimize an energy function knowing all the potentials. In this paper, we focus on CRFs where the computational cost of instanti- ating the potentials is orders of magnitude higher than MAP inference. This is often the case in semantic image segmen- tation, where most potentials are instantiated by slow clas- sifiers fed with costly features. We introduce Active MAP in- ference 1) to on-the-fly select a subset of potentials to be in- stantiated in the energy function, leaving the rest of the pa- rameters of the potentials unknown, and 2) to estimate the MAP labeling from such incomplete energy function. Re- sults for semantic segmentation benchmarks, namely PAS- CAL VOC 2010 [5] and MSRC-21 [19], show that Active MAP inference achieves similar levels of accuracy but with major efficiency gains.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Detecting Avocados to Zucchinis: What Have We Done, and Where Are We Going? [pdf]
Olga Russakovsky, Jia Deng, Zhiheng Huang, Alexander C. Berg, Li Fei-Fei |
|
Abstract: The growth of detection datasets and the multiple direc- tions of object detection research provide both an unprece- dented need and a great opportunity for a thorough evalu- ation of the current state of the field of categorical object detection. In this paper we strive to answer two key ques- tions. First, where are we currently as a field: what have we done right, what still needs to be improved? Second, where should we be going in designing the next generation of object detectors?
Inspired by the recent work of Hoiem et al. [10] on the standard PASCAL VOC detection dataset, we perform a large-scale study on the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) data. First, we quanti- tatively demonstrate that this dataset provides many of the same detection challenges as the PASCAL VOC. Due to its scale of 1000 object categories, ILSVRC also provides an excellent testbed for understanding the performance of de- tectors as a function of several key properties of the object classes. We conduct a series of analyses looking at how different detection methods perform on a number of image- level and object-class-level properties such as texture, color, deformation, and clutter. We learn important lessons of the current object detection methods and propose a number of insights for designing the next generation object detectors.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Spoken Attributes: Mixing Binary and Relative Attributes to Say the Right Thing [pdf]
Amir Sadovnik, Andrew Gallagher, Devi Parikh, Tsuhan Chen |
|
Abstract: In recent years, there has been a great deal of progress in describing objects with attributes. Attributes have proven useful for object recognition, image search, face verifica- tion, image description, and zero-shot learning. Typically, attributes are either binary or relative: they describe either the presence or absence of a descriptive characteristic, or the relative magnitude of the characteristic when compar- ing two exemplars. However, prior work fails to model the actual way in which humans use these attributes in descrip- tive statements of images. Specifically, it does not address the important interactions between the binary and relative aspects of an attribute. In this work we propose a spoken at- tribute classifier which models a more natural way of using an attribute in a description. For each attribute we train a classifier which captures the specific way this attribute should be used. We show that as a result of using this model, we produce descriptions about images of people that are more natural and specific than past systems.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Recursive Estimation of the Stein Center of SPD Matrices and Its Applications [pdf]
Hesamoddin Salehian, Guang Cheng, Baba C. Vemuri, Jeffrey Ho |
|
Abstract: Symmetric positive-definite (SPD) matrices are ubiqui- tous in Computer Vision, Machine Learning and Medical Image Analysis. Finding the center/average of a population of such matrices is a common theme in many algorithms such as clustering, segmentation, principal geodesic analy- sis, etc. The center of a population of such matrices can be defined using a variety of distance/divergence measures as the minimizer of the sum of squared distances/divergences from the unknown center to the members of the popula- tion. It is well known that the computation of the Karcher mean for the space of SPD matrices which is a negatively- curved Riemannian manifold is computationally expensive. Recently, the LogDet divergence-based center was shown to be a computationally attractive alternative. However, the LogDet-based mean of more than two matrices can not be computed in closed form, which makes it computation- ally less attractive for large populations. In this paper we present a novel recursive estimator for center based on the Stein distance which is the square root of the LogDet di- vergence that is significantly faster than the batch mode computation of this center. The key theoretical contribution is a closed-form solution for the weighted Stein center of two SPD matrices, which is used in the recursive computa- tion of the Stein center for a population of SPD matrices. Additionally, we show experimental evidence of the conver- gence of our recursive Stein center estimator to th
|
Similar papers:
[rank all papers by similarity to this]
|
|
Heterogeneous Auto-similarities of Characteristics (HASC): Exploiting Relational Information for Classification [pdf]
Marco San_Biagio, Marco Crocco, Marco Cristani, Samuele Martelli, Vittorio Murino |
|
Abstract: Capturing the essential characteristics of visual ob- jects by considering how their features are inter-related is a recent philosophy of object classification. In this pa- per, we embed this principle in a novel image descriptor, dubbed Heterogeneous Auto-Similarities of Characteristics (HASC). HASC is applied to heterogeneous dense features maps, encoding linear relations by covariances and non- linear associations through information-theoretic measures such as mutual information and entropy. In this way, highly complex structural information can be expressed in a com- pact, scale invariant and robust manner. The effectiveness of HASC is tested on many diverse detection and classifi- cation scenarios, considering objects, textures and pedes- trians, on widely known benchmarks (Caltech-101, Bro- datz, Daimler Multi-Cue). In all the cases, the results ob- tained with standard classifiers demonstrate the superiority of HASC with respect to the most adopted local feature de- scriptors nowadays, such as SIFT, HOG, LBP and feature covariances. In addition, HASC sets the state-of-the-art on the Brodatz texture dataset and the Daimler Multi-Cue pedestrian dataset, without exploiting ad-hoc sophisticated classifiers.
|
Similar papers:
[rank all papers by similarity to this]
|
|
3DNN: Viewpoint Invariant 3D Geometry Matching for Scene Understanding [pdf]
Scott Satkin, Martial Hebert |
|
Abstract: We present a new algorithm 3DNN (3D Nearest- Neighbor), which is capable of matching an image with 3D data, independently of the viewpoint from which the image was captured. By leveraging rich annotations associated with each image, our algorithm can automatically produce precise and detailed 3D models of a scene from a single im- age. Moreover, we can transfer information across images to accurately label and segment objects in a scene.
The true benefit of 3DNN compared to a traditional 2D nearest-neighbor approach is that by generalizing across viewpoints, we free ourselves from the need to have training examples captured from all possible viewpoints. Thus, we are able to achieve comparable results using orders of mag- nitude less data, and recognize objects from never-before- seen viewpoints. In this work, we describe the 3DNN algo- rithm and rigorously evaluate its performance for the tasks of geometry estimation and object detection/segmentation. By decoupling the viewpoint and the geometry of an image, we develop a scene matching approach which is truly 100% viewpoint invariant, yielding state-of-the-art performance on challenging data.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: A huge fraction of cameras used nowadays is based on CMOS sensors with a rolling shutter that exposes the image line by line. For dynamic scenes/cameras this introduces undesired effects like stretch, shear and wobble. It has been shown earlier that rotational shake induced rolling shutter effects in hand-held cell phone capture can be compensated based on an estimate of the camera rotation. In contrast, we analyse the case of significant camera motion, e.g. where a bypassing streetlevel capture vehicle uses a rolling shut- ter camera in a 3D reconstruction framework. The intro- duced error is depth dependent and cannot be compensated based on camera motion/rotation alone, invalidating also rectification for stereo camera systems. On top, significant lens distortion as often present in wide angle cameras in- tertwines with rolling shutter effects as it changes the time at which a certain 3D point is seen. We show that naive 3D reconstructions (assuming global shutter) will deliver biased geometry already for very mild assumptions on ve- hicle speed and resolution. We then develop rolling shutter dense multiview stereo algorithms that solve for time of ex- posure and depth at the same time, even in the presence of lens distortion and perform an evaluation on ground truth laser scan models as well as on real street-level data.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Fast Face Detector Training Using Tailored Views [pdf]
Kristina Scherbaum, James Petterson, Rogerio S. Feris, Volker Blanz, Hans-Peter Seidel |
|
Abstract: Face detection is an important task in computer vision and often serves as the first step for a variety of applications. State-of-the-art approaches use efficient learning algorithms and train on large amounts of manually labeled imagery. Acquiring appropriate training images, however, is very time-consuming and does not guarantee that the collected training data is representative in terms of data variability. Moreover, available data sets are often acquired under con- trolled settings, restricting, for example, scene illumination or 3D head pose to a narrow range. This paper takes a look into the automated generation of adaptive training samples from a 3D morphable face model. Using statistical insights, the tailored training data guarantees full data variability and is enriched by arbitrary facial attributes such as age or body weight. Moreover, it can automatically adapt to environmental constraints, such as illumination or viewing angle of recorded video footage from surveillance cameras. We use the tailored imagery to train a new many-core imple- mentation of Viola Jones AdaBoost object detection frame- work. The new implementation is not only faster but also enables the use of multiple feature channels such as color features at training time. In our experiments we trained seven view-dependent face detectors and evaluate these on the Face Detection Data Set and Benchmark (FDDB). Our experiments show that the use of tailored training imagery outperforms state-of-the-art
|
Similar papers:
[rank all papers by similarity to this]
|
|
Conservation Tracking [pdf]
Martin Schiegg, Philipp Hanslovsky, Bernhard X. Kausler, Lars Hufnagel, Fred A. Hamprecht |
|
Abstract: The quality of any tracking-by-assignment hinges on the accuracy of the foregoing target detection / segmentation step. In many kinds of images, errors in this first stage are unavoidable. These errors then propagate to, and corrupt, the tracking result.
Our main contribution is the first probabilistic graphical model that can explicitly account for over- and underseg- mentation errors even when the number of tracking targets is unknown and when they may divide, as in cell cultures. The tracking model we present implements global consis- tency constraints for the number of targets comprised by each detection and is solved to global optimality on reason- ably large 2D+t and 3D+t datasets. In addition, we em- pirically demonstrate the effectiveness of a postprocessing that allows to establish target identity even across occlusion / undersegmentation. The usefulness and efficiency of this new tracking method is demonstrated on three different and challenging 2D+t and 3D+t datasets from developmental biology.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Alternating Regression Forests for Object Detection and Pose Estimation [pdf]
Samuel Schulter, Christian Leistner, Paul Wohlhart, Peter M. Roth, Horst Bischof |
|
Abstract: We present Alternating Regression Forests (ARFs), a novel regression algorithm that learns a Random Forest by optimizing a global loss function over all trees. This inter- relates the information of single trees during the training phase and results in more accurate predictions. ARFs can minimize any differentiable regression loss without sacri- ficing the appealing properties of Random Forests, like low computational complexity during both, training and testing. Inspired by recent developments for classification [19], we derive a new algorithm capable of dealing with different regression loss functions, discuss its properties and investi- gate the relations to other methods like Boosted Trees.
We evaluate ARFs on standard machine learning bench- marks, where we observe better generalization power com- pared to both standard Random Forests and Boosted Trees. Moreover, we apply the proposed regressor to two computer vision applications: object detection and head pose estima- tion from depth images. ARFs outperform the Random For- est baselines in both tasks, illustrating the importance of optimizing a common loss function for all trees.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Box in the Box: Joint 3D Layout and Object Reasoning from Single Images [pdf]
Alexander G. Schwing, Sanja Fidler, Marc Pollefeys, Raquel Urtasun |
|
Abstract: Sanja Fidler TTI Chicago fidler@ttic.edu
Marc Pollefeys ETH Zurich pomarc@inf.ethz.ch
Raquel Urtasun TTI Chicago rurtasun@ttic.edu
GT
In this paper we propose an approach to jointly infer the room layout as well as the objects present in the scene. To- wards this goal, we propose a branch and bound algorithm which is guaranteed to retrieve the global optimum of the joint problem. The main difficulty resides in taking into account occlusion in order to not over-count the evidence. We introduce a new decomposition method, which general- izes integral geometry to triangular shapes, and allows us to bound the different terms in constant time. We exploit both geometric cues and object detectors as image features and show large improvements in 2D and 3D object detec- tion over state-of-the-art deformable part-based models.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Latent Data Association: Bayesian Model Selection for Multi-target Tracking [pdf]
Aleksandr V. Segal, Ian Reid |
|
Abstract: We propose a novel parametrization of the data asso- ciation problem for multi-target tracking. In our formula- tion, the number of targets is implicitly inferred together with the data association, effectively solving data associ- ation and model selection as a single inference problem. The novel formulation allows us to interpret data associa- tion and tracking as a single Switching Linear Dynamical System (SLDS). We compute an approximate posterior solu- tion to this problem using a dynamic programming/message passing technique. This inference-based approach allows us to incorporate richer probabilistic models into the track- ing system. In particular, we incorporate inference over in- liers/outliers and track termination times into the system. We evaluate our approach on publicly available datasets and demonstrate results competitive with, and in some cases exceeding the state of the art.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Predicting an Object Location Using a Global Image Representation [pdf]
Jose A. Rodriguez Serrano, Diane Larlus |
|
Abstract: We tackle the detection of prominent objects in images as a retrieval task: given a global image descriptor, we find the most similar images in an annotated dataset, and trans- fer the object bounding boxes. We refer to this approach as data driven detection (DDD), that is an alternative to sliding windows. Previous works have used similar notions but with task-independent similarities and representations, i.e. they were not tailored to the end-goal of localization. This article proposes two contributions: (i) a metric learn- ing algorithm and (ii) a representation of images as object probability maps, that are both optimized for detection. We show experimentally that these two contributions are cru- cial to DDD, do not require costly additional operations, and in some cases yield comparable or better results than state-of-the-art detectors despite conceptual simplicity and increased speed. As an application of prominent object detection, we improve fine-grained categorization by pre- cropping images with the proposed approach.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Image Segmentation with Cascaded Hierarchical Models and Logistic Disjunctive Normal Networks [pdf]
Mojtaba Seyedhosseini, Mehdi Sajjadi, Tolga Tasdizen |
|
Abstract: Contextual information plays an important role in solv- ing vision problems such as image segmentation. However, extracting contextual information and using it in an effec- tive way remains a difficult problem. To address this chal- lenge, we propose a multi-resolution contextual framework, called cascaded hierarchical model (CHM), which learns contextual information in a hierarchical framework for im- age segmentation. At each level of the hierarchy, a classifier is trained based on downsampled input images and outputs of previous levels. Our model then incorporates the result- ing multi-resolution contextual information into a classifier to segment the input image at original resolution. We repeat this procedure by cascading the hierarchical framework to improve the segmentation accuracy. Multiple classifiers are learned in the CHM; therefore, a fast and accurate clas- sifier is required to make the training tractable. The clas- sifier also needs to be robust against overfitting due to the large number of parameters learned during training. We introduce a novel classification scheme, called logistic dis- junctive normal networks (LDNN), which consists of one adaptive layer of feature detectors implemented by logis- tic sigmoid functions followed by two fixed layers of logical units that compute conjunctions and disjunctions, respec- tively. We demonstrate that LDNN outperforms state-of-the- art classifiers and can be used in the CHM to improve object segmentation performanc
|
Similar papers:
[rank all papers by similarity to this]
|
|
Semantic Transform: Weakly Supervised Semantic Inference for Relating Visual Attributes [pdf]
Sukrit Shankar, Joan Lasenby, Roberto Cipolla |
|
Abstract: Relative (comparative) attributes are promising for the- matic ranking of visual entities, which also aids in recog- nition tasks [19, 23]. However, attribute rank learning of- ten requires a substantial amount of relational supervision, which is highly tedious, and apparently impractical for real- world applications.
In this paper, we introduce the Semantic Transform, which under minimal supervision, adaptively finds a seman- tic feature space along with a class ordering that is related in the best possible way. Such a semantic space is found for every attribute category. To relate the classes under weak supervision, the class ordering needs to be refined accord- ing to a cost function in an iterative procedure. This prob- lem is ideally NP-hard, and we thus propose a constrained search tree formulation for the same.
Driven by the adaptive semantic feature space represen- tation, our model achieves the best results to date for all of the tasks of relative, absolute and zero-shot classification on two popular datasets.
|
Similar papers:
[rank all papers by similarity to this]
|
|
What Do You Do? Occupation Recognition in a Photo via Social Context [pdf]
Ming Shao, Liangyue Li, Yun Fu |
|
Abstract: In this paper, we investigate the problem of recogniz- ing occupations of multiple people with arbitrary poses in a photo. Previous work utilizing single persons nearly frontal clothing information and fore/background contex- t preliminarily proves that occupation recognition is com- putationally feasible in computer vision. However, in prac- tice, multiple people with arbitrary poses are common in a photo, and recognizing their occupations is even more challenging. We argue that with appropriately built visual attributes, co-occurrence, and spatial configuration mod- el that is learned through structure SVM, we can recog- nize multiple peoples occupations in a photo simultane- ously. To evaluate our methods performance, we conduc- t extensive experiments on a new well-labeled occupation database with 14 representative occupations and over 7K images. Results on this database validate our methods ef- fectiveness and show that occupation recognition is solv- able in a more general case.
|
Similar papers:
[rank all papers by similarity to this]
|
|
A New Adaptive Segmental Matching Measure for Human Activity Recognition [pdf]
Shahriar Shariat, Vladimir Pavlovic |
|
Abstract: The problem of human activity recognition is a cen- tral problem in many real-world applications. In this pa- per we propose a fast and effective segmental alignment- based method that is able to classify activities and interac- tions in complex environments. We empirically show that such model is able to recover the alignment that leads to improved similarity measures within sequence classes and hence, raises the classification performance. We also apply a bounding technique on the histogram distances to reduce the computation of the otherwise exhaustive search.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Learning to Rank Using Privileged Information [pdf]
Viktoriia Sharmanska, Novi Quadrianto, Christoph H. Lampert |
|
Abstract: Many computer vision problems have an asymmetric dis- tribution of information between training and test time. In this work, we study the case where we are given additional information about the training data, which however will not be available at test time. This situation is called learn- ing using privileged information (LUPI). We introduce two maximum-margin techniques that are able to make use of this additional source of information, and we show that the framework is applicable to several scenarios that have been studied in computer vision before. Experiments with at- tributes, bounding boxes, image tags and rationales as ad- ditional information in object classification show promising results.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Bayesian Joint Topic Modelling for Weakly Supervised Object Localisation [pdf]
Zhiyuan Shi, Timothy M. Hospedales, Tao Xiang |
|
Abstract: We address the problem of localisation of objects as bounding boxes in images with weak labels. This weakly su- pervised object localisation problem has been tackled in the past using discriminative models where each object class is localised independently from other classes. We propose a novel framework based on Bayesian joint topic modelling. Our framework has three distinctive advantages over pre- vious works: (1) All object classes and image backgrounds are modelled jointly together in a single generative model so that explaining away inference can resolve ambigu- ity and lead to better learning and localisation. (2) The Bayesian formulation of the model enables easy integration of prior knowledge about object appearance to compensate for limited supervision. (3) Our model can be learned with a mixture of weakly labelled and unlabelled data, allowing the large volume of unlabelled images on the Internet to be exploited for learning. Extensive experiments on the chal- lenging VOC dataset demonstrate that our approach out- performs the state-of-the-art competitors.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: We propose a co-detection and labeling (CoDeL) frame- work to identify persons that contain self-consistent ap- pearance in multiple images. Our CoDeL model builds upon the deformable part-based model to detect human hypotheses and exploits cross-image correspondence via a matching classifier. Relying on a Gaussian process, this matching classifier models the similarity of two hypotheses and efficiently captures the relative importance contributed by various visual features, reducing the adverse effect of scattered occlusion. Further, the detector and matching classifier together make our model fit into a semi-supervised co-training framework, which can get enhanced results with a small amount of labeled training data. Our CoDeL model achieves decent performance on existing and new benchmark datasets.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Robust Trajectory Clustering for Motion Segmentation [pdf]
Feng Shi, Zhong Zhou, Jiangjian Xiao, Wei Wu |
|
Abstract: Due to occlusions and objects non-rigid deformation in the scene, the obtained motion trajectories from common trackers may contain a number of missing or mis-associated entries. To cluster such corrupted point based trajectories into multiple motions is still a hard problem. In this paper, we present an approach that exploits temporal and spatial characteristics from tracked points to facilitate segmenta- tion of incomplete and corrupted trajectories, thereby ob- tain highly robust results against severe data missing and noises. Our method first uses the Discrete Cosine Trans- form (DCT) bases as a temporal smoothness constraint on trajectory projection to ensure the validity of resulting com- ponents to repair pathological trajectories. Then, based on an observation that the trajectories of foreground and back- ground in a scene may have different spatial distributions, we propose a two-stage clustering strategy that first per- forms foreground-background separation then segments re- maining foreground trajectories. We show that, with this new clustering strategy, sequences with complex motions can be accurately segmented by even using a simple trans- lational model. Finally, a series of experiments on Hopkins 155 dataset and Berkeley motion segmentation dataset show the advantage of our method over other state-of-the-art mo- tion segmentation algorithms in terms of both effectiveness and robustness.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Joint Noise Level Estimation from Personal Photo Collections [pdf]
Yichang Shih, Vivek Kwatra, Troy Chinen, Hui Fang, Sergey Ioffe |
|
Abstract: Personal photo albums are heavily biased towards faces of people, but most state-of-the-art algorithms for image de- noising and noise estimation do not exploit facial informa- tion. We propose a novel technique for jointly estimating noise levels of all face images in a photo collection. Pho- tos in a personal album are likely to contain several faces of the same people. While some of these photos would be clean and high quality, others may be corrupted by noise. Our key idea is to estimate noise levels by comparing mul- tiple images of the same content that differ predominantly in their noise content. Specifically, we compare geometri- cally and photometrically aligned face images of the same person.
Our estimation algorithm is based on a probabilistic for- mulation that seeks to maximize the joint probability of es- timated noise levels across all images. We propose an ap- proximate solution that decomposes this joint maximization into a two-stage optimization. The first stage determines the relative noise between pairs of images by pooling es- timates from corresponding patch pairs in a probabilistic fashion. The second stage then jointly optimizes for all ab- solute noise parameters by conditioning them upon relative noise levels, which allows for a pairwise factorization of the probability distribution. We evaluate our noise estima- tion method using quantitative experiments to measure ac- curacy on synthetic data. Additionally, we employ the esti- mated noise levels fo
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: This paper proposes a novel part-based representation for modeling object categories. Our representation com- bines the effectiveness of deformable part-based models with the richness of geometric representation by defining parts based on consistent underlying 3D geometry. Our key hypothesis is that while the appearance and the arrange- ment of parts might vary across the instances of object cat- egories, the constituent parts will still have consistent un- derlying 3D geometry. We propose to learn this geometry- driven deformable part-based model (gDPM) from a set of labeled RGBD images. We also demonstrate how the geometric representation of gDPM can help us leverage depth data during training and constrain the latent model learning problem. But most importantly, a joint geometric and appearance based representation not only allows us to achieve state-of-the-art results on object detection but also allows us to tackle the grand challenge of understanding 3D objects from 2D images.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: While saliency in images has been extensively studied in recent years, there is very little work on saliency of point sets. This is despite the fact that point sets and range data are becoming ever more widespread and have myriad appli- cations. In this paper we present an algorithm for detecting the salient points in unorganized 3D point sets. Our algo- rithm is designed to cope with extremely large sets, which may contain tens of millions of points. Such data is typi- cal of urban scenes, which have recently become commonly available on the web. No previous work has handled such data. For general data sets, we show that our results are competitive with those of saliency detection of surfaces, al- though we do not have any connectivity information. We demonstrate the utility of our algorithm in two applications: producing a set of the most informative viewpoints and sug- gesting an informative city tour given a city scan.
|
Similar papers:
[rank all papers by similarity to this]
|
|
A Fully Hierarchical Approach for Finding Correspondences in Non-rigid Shapes [pdf]
Ivan Sipiran, Benjamin Bustos |
|
Abstract: This paper presents a hierarchical method for finding correspondences in non-rigid shapes. We propose a new representation for 3D meshes: the decomposition tree. This structure characterizes the recursive decomposition process of a mesh into regions of interest and keypoints. The inter- nal nodes contain regions of interest (which may be recur- sively decomposed) and the leaf nodes contain the keypoints to be matched. We also propose a hierarchical matching al- gorithm that performs in a level-wise manner. The match- ing process is guided by the similarity between regions in high levels of the tree, until reaching the keypoints stored in the leaves. This allows us to reduce the search space of correspondences, making also the matching process effi- cient. We evaluate the effectiveness of our approach using the SHREC2010 robust correspondence benchmark. In ad- dition, we show that our results outperform the state of the art.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Tracking Revisited Using RGBD Camera: Unified Benchmark and Baselines [pdf]
Shuran Song, Jianxiong Xiao |
|
Abstract: Despite significant progress, tracking is still considered to be a very challenging task. Recently, the increasing pop- ularity of depth sensors has made it possible to obtain reli- able depth easily. This may be a game changer for tracking, since depth can be used to prevent model drift and han- dle occlusion. We also observe that current tracking al- gorithms are mostly evaluated on a very small number of videos collected and annotated by different groups. The lack of a reasonable size and consistently constructed bench- mark has prevented a persuasive comparison among dif- ferent algorithms. In this paper, we construct a unified benchmark dataset of 100 RGBD videos with high diver- sity, propose different kinds of RGBD tracking algorithms using 2D or 3D model, and present a quantitative com- parison of various algorithms with RGB or RGBD input. We aim to lay the foundation for further research in both RGB and RGBD tracking, and our benchmark is available at http://tracking.cs.princeton.edu.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Interactive Markerless Articulated Hand Motion Tracking Using RGB and Depth Data [pdf]
Srinath Sridhar, Antti Oulasvirta, Christian Theobalt |
|
Abstract: Tracking the articulated 3D motion of the hand has im- portant applications, for example, in humancomputer in- teraction and teleoperation. We present a novel method that can capture a broad range of articulated hand motions at interactive rates. Our hybrid approach combines, in a voting scheme, a discriminative, part-based pose retrieval method with a generative pose estimation method based on local optimization. Color information from a multi- view RGB camera setup along with a person-specific hand model are used by the generative method to find the pose that best explains the observed images. In parallel, our discriminative pose estimation method uses fingertips de- tected on depth data to estimate a complete or partial pose of the hand by adopting a part-based pose retrieval strat- egy. This part-based strategy helps reduce the search space drastically in comparison to a global pose retrieval strat- egy. Quantitative results show that our method achieves state-of-the-art accuracy on challenging sequences and a near-realtime performance of 10 fps on a desktop computer.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Synergistic Clustering of Image and Segment Descriptors for Unsupervised Scene Understanding [pdf]
Daniel M. Steinberg, Oscar Pizarro, Stefan B. Williams |
|
Abstract: With the advent of cheap, high fidelity, digital imaging systems, the quantity and rate of generation of visual data can dramatically outpace a humans ability to label or an- notate it. In these situations there is scope for the use of unsupervised approaches that can model these datasets and automatically summarise their content. To this end, we present a totally unsupervised, and annotation-less, model for scene understanding. This model can simultaneously cluster whole-image and segment descriptors, thereby form- ing an unsupervised model of scenes and objects. We show that this model outperforms other unsupervised models that can only cluster one source of information (image or seg- ment) at once. We are able to compare unsupervised and su- pervised techniques using standard measures derived from confusion matrices and contingency tables. This shows that our unsupervised model is competitive with current super- vised and weakly-supervised models for scene understand- ing on standard datasets. We also demonstrate our model operating on a dataset with more than 100,000 images col- lected by an autonomous underwater vehicle.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Large-Scale Multi-resolution Surface Reconstruction from RGB-D Sequences [pdf]
Frank Steinbrucker, Christian Kerl, Daniel Cremers |
|
Abstract: We propose a method to generate highly detailed, tex- tured 3D models of large environments from RGB-D se- quences. Our system runs in real-time on a standard desk- top PC with a state-of-the-art graphics card. To reduce the memory consumption, we fuse the acquired depth maps and colors in a multi-scale octree representation of a signed dis- tance function. To estimate the camera poses, we construct a pose graph and use dense image alignment to determine the relative pose between pairs of frames. We add edges be- tween nodes when we detect loop-closures and optimize the pose graph to correct for long-term drift. Our implementa- tion is highly parallelized on graphics hardware to achieve real-time performance. More specifically, we can recon- struct, store, and continuously update a colored 3D model of an entire corridor of nine rooms at high levels of detail in real-time on a single GPU with 2.5GB.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Weakly Supervised Learning of Image Partitioning Using Decision Trees with Structured Split Criteria [pdf]
Christoph Straehle, Ullrich Koethe, Fred A. Hamprecht |
|
Abstract: We propose a scheme that allows to partition an image into a previously unknown number of segments, using only minimal supervision in terms of a few must-link and cannot- link annotations. We make no use of regional data terms, learning instead what constitutes a likely boundary between segments. Since boundaries are only implicitly specified through cannot-link constraints, this is a hard and noncon- vex latent variable problem. We address this problem in a greedy fashion using a randomized decision tree on features associated with interpixel edges. We use a structured pu- rity criterion during tree construction and also show how a backtracking strategy can be used to prevent the greedy search from ending up in poor local optima. The proposed strategy is compared with prior art on natural images.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Shortest Paths with Curvature and Torsion [pdf]
Petter Strandmark, Johannes Ulen, Fredrik Kahl, Leo Grady |
|
Abstract: This paper describes a method of finding thin, elongated structures in images and volumes. We use shortest paths to minimize very general functionals of higher-order curve properties, such as curvature and torsion. Our globally op- timal method uses line graphs and its runtime is polynomial in the size of the discretization, often in the order of sec- onds on a single computer. To our knowledge, we are the first to perform experiments in three dimensions with cur- vature and torsion regularization. The largest graphs we process have almost one hundred billion arcs. Experiments on medical images and in multi-view reconstruction show the significance and practical usefulness of regularization based on curvature while torsion is still only tractable for small-scale problems.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Tree Shape Priors with Connectivity Constraints Using Convex Relaxation on General Graphs [pdf]
Jan Stuhmer, Peter Schroder, Daniel Cremers |
|
Abstract: We propose a novel method to include a connectivity prior into image segmentation that is based on a binary la- beling of a directed graph, in this case a geodesic shortest path tree. Specifically we make two contributions: First, we construct a geodesic shortest path tree with a distance mea- sure that is related to the image data and the bending energy of each path in the tree. Second, we include a connectiv- ity prior in our segmentation model, that allows to segment not only a single elongated structure, but instead a whole connected branching tree. Because both our segmentation model and the connectivity constraint are convex, a global optimal solution can be found. To this end, we generalize a recent primal-dual algorithm for continuous convex op- timization to an arbitrary graph structure. To validate our method we present results on data from medical imaging in angiography and retinal blood vessel segmentation.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Linear Sequence Discriminant Analysis: A Model-Based Dimensionality Reduction Method for Vector Sequences [pdf]
Bing Su, Xiaoqing Ding |
|
Abstract: Dimensionality reduction for vectors in sequences is challenging since labels are attached to sequences as a w- hole. This paper presents a model-based dimensionality reduction method for vector sequences, namely linear se- quence discriminant analysis (LSDA), which attempts to find a subspace in which sequences of the same class are projected together while those of different classes are pro- jected as far as possible. For each sequence class, an H- MM is built from states of which statistics are extracted. Means of these states are linked in order to form a mean sequence, and the variance of the sequence class is defined as the sum of all variances of component states. LSDA then learns a transformation by maximizing the separability be- tween sequence classes and at the same time minimizing the within-sequence class scatter. DTW distance between mean sequences is used to measure the separability between se- quence classes. We show that the optimization problem can be approximately transformed into an eigen decomposition problem. LDA can be seen as a special case of LSDA by considering non-sequential vectors as sequences of length one. The effectiveness of the proposed LSDA is demonstrat- ed on two individual sequence datasets from UCI machine learning repository as well as two concatenate sequence datasets: APTI Arabic printed text database and IFN/ENIT Arabic handwriting database.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: The goal of high level event classification from videos is to assign a single, high level event label to each query video. Traditional approaches represent each video as a set of low level features and encode it into a fixed length feature vector (e.g. Bag-of-Words), which leave a big gap between low level visual features and high level events. Our paper tries to address this problem by exploiting activity concept transitions in video events (ACTIVE). A video is treated as a sequence of short clips, all of which are observations cor- responding to latent activity concept variables in a Hidden Markov Model (HMM). We propose to apply Fisher Ker- nel techniques so that the concept transitions over time can be encoded into a compact and fixed length feature vector very efficiently. Our approach can utilize concept annota- tions from independent datasets, and works well even with a very small number of training samples. Experiments on the challenging NIST TRECVID Multimedia Event Detec- tion (MED) dataset shows our approach performs favorably over the state-of-the-art.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Find the Best Path: An Efficient and Accurate Classifier for Image Hierarchies [pdf]
Min Sun, Wan Huang, Silvio Savarese |
|
Abstract: Many methods have been proposed to solve the image classification problem for a large number of categories. Among them, methods based on tree-based representations achieve good trade-off between accuracy and test time ef- ficiency. While focusing on learning a tree-shaped hierar- chy and the corresponding set of classifiers, most of them [11, 2, 14] use a greedy prediction algorithm for test time efficiency. We argue that the dramatic decrease in accuracy at high efficiency is caused by the specific design choice of the learning and greedy prediction algorithms. In this work, we propose a classifier which achieves a better trade-off between efficiency and accuracy with a given tree-shaped hierarchy. First, we convert the classification problem as finding the best path in the hierarchy, and a novel branch- and-bound-like algorithm is introduced to efficiently search for the best path. Second, we jointly train the classifiers us- ing a novel Structured SVM (SSVM) formulation with addi- tional bound constraints. As a result, our method achieves a significant 4.65%, 5.43%, and 4.07% (relative 24.82%, 41.64%, and 109.79%) improvement in accuracy at high efficiency compared to state-of-the-art greedy tree-based methods [14] on Caltech-256 [15], SUN [32] and ImageNet 1K [9] dataset, respectively. Finally, we show that our branch-and-bound-like algorithm naturally ranks the paths in the hierarchy (Fig. 8) so that users can further process them.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: This paper proposes a hybrid convolutional network (ConvNet)-Restricted Boltzmann Machine (RBM) model for face verification in wild conditions. A key contribution of this work is to directly learn relational visual features, which indicate identity similarities, from raw pixels of face pairs with a hybrid deep network. The deep ConvNets in our model mimic the primary visual cortex to jointly extract local relational visual features from two face images compared with the learned filter pairs. These relational features are further processed through multiple layers to extract high-level and global features. Multiple groups of ConvNets are constructed in order to achieve robustness and characterize face similarities from different aspects. The top-layer RBM performs inference from complementary high-level features extracted from different ConvNet groups with a two-level average pooling hierarchy. The entire hybrid deep network is jointly fine-tuned to optimize for the task of face verification. Our model achieves competitive face verification performance on the LFW dataset.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Learning Discriminative Part Detectors for Image Classification and Cosegmentation [pdf]
Jian Sun, Jean Ponce |
|
Abstract: In this paper, we address the problem of learning dis- criminative part detectors from image sets with category labels. We propose a novel latent SVM model regularized by group sparsity to learn these part detectors. Starting from a large set of initial parts, the group sparsity regular- izer forces the model to jointly select and optimize a set of discriminative part detectors in a max-margin framework. We propose a stochastic version of a proximal algorithm to solve the corresponding optimization problem. We apply the proposed method to image classification and cosegmen- tation, and quantitative experiments with standard bench- marks show that it matches or improves upon the state of the art.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Pyramid Coding for Functional Scene Element Recognition in Video Scenes [pdf]
Eran Swears, Anthony Hoogs, Kim Boyer |
|
Abstract: Recognizing functional scene elements in video scenes based on the behaviors of moving objects that interact with them is an emerging problem of interest. Existing approaches have a limited ability to characterize elements such as cross-walks, intersections, and buildings that have low activity, are multi-modal, or have indirect evidence. Our approach recognizes the low activity and multi-model elements (crosswalks/intersections) by introducing a hierarchy of descriptive clusters to form a pyramid of codebooks that is sparse in the number of clusters and dense in content. The incorporation of local behavioral context such as person-enter-building and vehicle-parking nearby enables the detection of elements that do not have direct motion-based evidence, e.g. buildings. These two contributions significantly improve scene element recognition when compared against three state-of-the-art approaches. Results are shown on typical ground level surveillance video and for the first time on the more complex Wide Area Motion Imagery.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Distributed Low-Rank Subspace Segmentation [pdf]
Ameet Talwalkar, Lester Mackey, Yadong Mu, Shih-Fu Chang, Michael I. Jordan |
|
Abstract: Vision problems ranging from image clustering to mo- tion segmentation to semi-supervised learning can naturally be framed as subspace segmentation problems, in which one aims to recover multiple low-dimensional subspaces from noisy and corrupted input data. Low-Rank Repre- sentation (LRR), a convex formulation of the subspace seg- mentation problem, is provably and empirically accurate on small problems but does not scale to the massive sizes of modern vision datasets. Moreover, past work aimed at scaling up low-rank matrix factorization is not applicable to LRR given its non-decomposable constraints. In this work, we propose a novel divide-and-conquer algorithm for large-scale subspace segmentation that can cope with LRRs non-decomposable constraints and maintains LRRs strong recovery guarantees. This has immediate implica- tions for the scalability of subspace segmentation, which we demonstrate on a benchmark face recognition dataset and in simulations. We then introduce novel applications of LRR-based subspace segmentation to large-scale semi- supervised learning for multimedia event detection, concept detection, and image tagging. In each case, we obtain state- of-the-art results and order-of-magnitude speed ups.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Towards Motion Aware Light Field Video for Dynamic Scenes [pdf]
Salil Tambe, Ashok Veeraraghavan, Amit Agrawal |
|
Abstract: Current Light Field (LF) cameras offer fixed resolution in space, time and angle which is decided a-priori and is independent of the scene. These cameras either trade-off spatial resolution to capture single-shot LF [20, 27, 12] or tradeoff temporal resolution by assuming a static scene to capture high spatial resolution LF [18, 3]. Thus, captur- ing high spatial resolution LF video for dynamic scenes re- mains an open and challenging problem.
We present the concept, design and implementation of a LF video camera that allows capturing high resolution LF video. The spatial, angular and temporal resolution are not fixed a-priori and we exploit the scene-specific re- dundancy in space, time and angle. Our reconstruction is motion-aware and offers a continuum of resolution trade- off with increasing motion in the scene. The key idea is (a) to design efficient multiplexing matrices that allow resolu- tion tradeoffs, (b) use dictionary learning and sparse repre- sentations for robust reconstruction, and (c) perform local motion-aware adaptive reconstruction.
We perform extensive analysis and characterize the per- formance of our motion-aware reconstruction algorithm. We show realistic simulations using a graphics simulator as well as real results using a LCoS based programmable camera. We demonstrate novel results such as high resolu- tion digital refocusing for dynamic moving objects.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Combining the Right Features for Complex Event Recognition [pdf]
Kevin Tang, Bangpeng Yao, Li Fei-Fei, Daphne Koller |
|
Abstract: In this paper, we tackle the problem of combining fea- tures extracted from video for complex event recognition. Feature combination is an especially relevant task in video data, as there are many features we can extract, rang- ing from image features computed from individual frames to video features that take temporal information into ac- count. To combine features effectively, we propose a method that is able to be selective of different subsets of features, as some features or feature combinations may be unin- formative for certain classes. We introduce a hierarchi- cal method for combining features based on the AND/OR graph structure, where nodes in the graph represent com- binations of different sets of features. Our method auto- matically learns the structure of the AND/OR graph using score-based structure learning, and we introduce an infer- ence procedure that is able to efficiently compute structure scores. We present promising results and analysis on the difficult and large-scale 2011 TRECVID Multimedia Event Detection dataset [17].
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: Among image segmentation algorithms there are two major groups: (a) methods assuming known appearance models and (b) methods estimating appearance models jointly with segmentation. Typically, the first group opti- mizes appearance log-likelihoods in combination with some spacial regularization. This problem is relatively simple and many methods guarantee globally optimal results. The second group treats model parameters as additional vari- ables transforming simple segmentation energies into high- order NP-hard functionals (Zhu-Yuille, Chan-Vese, Grab- Cut, etc). It is known that such methods indirectly minimize the appearance overlap between the segments.
We propose a new energy term explicitly measuring L1 distance between the object and background appearance models that can be globally maximized in one graph cut. We show that in many applications our simple term makes NP-hard segmentation functionals unnecessary. Our one cut algorithm effectively replaces approximate iterative op- timization techniques based on block coordinate descent.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Learning People Detectors for Tracking in Crowded Scenes [pdf]
Siyu Tang, Mykhaylo Andriluka, Anton Milan, Konrad Schindler, Stefan Roth, Bernt Schiele |
|
Abstract: People tracking in crowded real-world scenes is chal- lenging due to frequent and long-term occlusions. Recent tracking methods obtain the image evidence from object (people) detectors, but typically use off-the-shelf detectors and treat them as black box components. In this paper we argue that for best performance one should explicitly train people detectors on failure cases of the overall tracker instead. To that end, we first propose a novel joint peo- ple detector that combines a state-of-the-art single person detector with a detector for pairs of people, which explic- itly exploits common patterns of person-person occlusions across multiple viewpoints that are a frequent failure case for tracking in crowded scenes. To explicitly address re- maining failure modes of the tracker we explore two meth- ods. First, we analyze typical failures of trackers and train a detector explicitly on these cases. And second, we train the detector with the people tracker in the loop, focusing on the most common tracker failures. We show that our joint multi-person detector significantly improves both de- tection accuracy as well as tracker performance, improving the state-of-the-art on standard benchmarks.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Real-Time Articulated Hand Pose Estimation Using Semi-supervised Transductive Regression Forests [pdf]
Danhang Tang, Tsz-Ho Yu, Tae-Kyun Kim |
|
Abstract: This paper presents the first semi-supervised transduc- tive algorithm for real-time articulated hand pose estima- tion. Noisy data and occlusions are the major challenges of articulated hand pose estimation. In addition, the dis- crepancies among realistic and synthetic pose data under- mine the performances of existing approaches that use syn- thetic data extensively in training. We therefore propose the Semi-supervised Transductive Regression (STR) forest which learns the relationship between a small, sparsely la- belled realistic dataset and a large synthetic dataset. We also design a novel data-driven, pseudo-kinematic tech- nique to refine noisy or occluded joints. Our contributions include: (i) capturing the benefits of both realistic and syn- thetic data via transductive learning; (ii) showing accura- cies can be improved by considering unlabelled data; and (iii) introducing a pseudo-kinematic technique to refine ar- ticulations efficiently. Experimental results show not only the promising performance of our method with respect to noise and occlusions, but also its superiority over state-of- the-arts in accuracy, robustness and speed.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Live Metric 3D Reconstruction on Mobile Phones [pdf]
Petri Tanskanen, Kalin Kolev, Lorenz Meier, Federico Camposeco, Olivier Saurer, Marc Pollefeys |
|
Abstract: 013 IEEE International Conference on Computer Vision
1550-5499/13 $31.00 2013 IEEE DOI 10.1109/ICCV.2013.15
65
The system is fully automatic and does not require markers or any other specific settings for initialization.
We perform feature-based tracking and mapping in real time but leverage full inertial sensing in position and orientation to estimate the metric scale of the re- constructed 3D models and to make the process more resilient to sudden motions.
The system offers an interactive interface for casual capture of scaled 3D models of real-world objects by non-experts. The approach leverages the inertial sen- sors to automatically select suitable keyframes when the phone is held still and uses the intermediate mo- tion to calculate scale. Visual and auditory feedback is provided to enable intuitive and fool-proof operation.
We propose an efficient and accurate multi-resolution scheme for dense stereo matching which makes use of the capabilities of the GPU and allows to reduce the computational time for each processed image to about 2-3 seconds.
2. Related Work
Our work is related to several fields in computer vision: visual inertial fusion, simultaneous localization and map- ping (SLAM) and image-based modeling.
Visual inertial fusion is a well established technique [1]. Lobo and Dias align depth maps of a stereo head using gravity as vertical reference in [6]. As their head is cal- ibrated, they do not utilize linear acceleration to recover scale. W
|
Similar papers:
[rank all papers by similarity to this]
|
|
Depth from Combining Defocus and Correspondence Using Light-Field Cameras [pdf]
Michael W. Tao, Sunil Hadap, Jitendra Malik, Ravi Ramamoorthi |
|
Abstract: 2Adobe
Light-field cameras have recently become available to the consumer market. An array of micro-lenses captures enough information that one can refocus images after ac- quisition, as well as shift ones viewpoint within the sub- apertures of the main lens, effectively obtaining multiple views. Thus, depth cues from both defocus and correspon- dence are available simultaneously in a single capture. Pre- viously, defocus could be achieved only through multiple image exposures focused at different depths, while corre- spondence cues needed multiple exposures at different view- points or multiple cameras; moreover, both cues could not easily be obtained together.
In this paper, we present a novel simple and principled algorithm that computes dense depth estimation by combin- ing both defocus and correspondence depth cues. We ana- lyze the x-u 2D epipolar image (EPI), where by convention we assume the spatial x coordinate is horizontal and the an- gular u coordinate is vertical (our final algorithm uses the full 4D EPI). We show that defocus depth cues are obtained by computing the horizontal (spatial) variance after ver- tical (angular) integration, and correspondence depth cues by computing the vertical (angular) variance. We then show how to combine the two cues into a high quality depth map, suitable for computer vision applications such as matting, full control of depth-of-field, and surface reconstruction.
|
Similar papers:
[rank all papers by similarity to this]
|
|
A Flexible Scene Representation for 3D Reconstruction Using an RGB-D Camera [pdf]
Diego Thomas, Akihiro Sugimoto |
|
Abstract: Updating a global 3D model with live RGB-D measure- ments has proven to be successful for 3D reconstruction of indoor scenes. Recently, a Truncated Signed Distance Function (TSDF) volumetric model and a fusion algorithm have been introduced (KinectFusion), showing significant advantages such as computational speed and accuracy of the reconstructed scene. This algorithm, however, is expen- sive in memory when constructing and updating the global model. As a consequence, the method is not well scalable to large scenes. We propose a new flexible 3D scene repre- sentation using a set of planes that is cheap in memory use and, nevertheless, achieves accurate reconstruction of in- door scenes from RGB-D image sequences. Projecting the scene onto different planes reduces significantly the size of the scene representation and thus it allows us to generate a global textured 3D model with lower memory requirement while keeping accuracy and easiness to update with live RGB-D measurements. Experimental results demonstrate that our proposed flexible 3D scene representation achieves accurate reconstruction, while keeping the scalability for large indoor scenes.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Hierarchical Data-Driven Descent for Efficient Optimal Deformation Estimation [pdf]
Yuandong Tian, Srinivasa G. Narasimhan |
|
Abstract: Real-world surfaces such as clothing, water and human body deform in complex ways. The image distortions ob- served are high-dimensional and non-linear, making it hard to estimate these deformations accurately. The recent data- driven descent approach [17] applies Nearest Neighbor es- timators iteratively on a particular distribution of training samples to obtain a globally optimal and dense deforma- tion field between a template and a distorted image. In this work, we develop a hierarchical structure for the Nearest Neighbor estimators, each of which can have only a local image support. We demonstrate in both theory and practice that this algorithm has several advantages over the non- hierarchical version: it guarantees global optimality with significantly fewer training samples, is several orders faster, provides a metric to decide whether a given image is hard (or easy) requiring more (or less) samples, and can han- dle more complex scenes that include both global motion and local deformation. The proposed algorithm success- fully tracks a broad range of non-rigid scenes including wa- ter, clothing, and medical images, and compares favorably against several other deformation estimation and tracking approaches that do not provide optimality guarantees.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Anchored Neighborhood Regression for Fast Example-Based Super-Resolution [pdf]
Radu Timofte, Vincent De_Smet, Luc Van_Gool |
|
Abstract: Recently there have been significant advances in image upscaling or image super-resolution based on a dictionary of low and high resolution exemplars. The running time of the methods is often ignored despite the fact that it is a crit- ical factor for real applications. This paper proposes fast super-resolution methods while making no compromise on quality. First, we support the use of sparse learned dic- tionaries in combination with neighbor embedding meth- ods. In this case, the nearest neighbors are computed us- ing the correlation with the dictionary atoms rather than the Euclidean distance. Moreover, we show that most of the current approaches reach top performance for the right parameters. Second, we show that using global collabo- rative coding has considerable speed advantages, reducing the super-resolution mapping to a precomputed projective matrix. Third, we propose the anchored neighborhood re- gression. That is to anchor the neighborhood embedding of a low resolution patch to the nearest atom in the dictio- nary and to precompute the corresponding embedding ma- trix. These proposals are contrasted with current state-of- the-art methods on standard images. We obtain similar or improved quality and one or two orders of magnitude speed improvements.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Breaking the Chain: Liberation from the Temporal Markov Assumption for Tracking Human Poses [pdf]
Ryan Tokola, Wongun Choi, Silvio Savarese |
|
Abstract: We present an approach to multi-target tracking that has expressive potential beyond the capabilities of chain- shaped hidden Markov models, yet has significantly reduced complexity. Our framework, which we call tracking-by- selection, is similar to tracking-by-detection in that it sepa- rates the tasks of detection and tracking, but it shifts tempo- ral reasoning from the tracking stage to the detection stage. The core feature of tracking-by-selection is that it reasons about path hypotheses that traverse the entire video instead of a chain of single-frame object hypotheses. A traditional chain-shaped tracking-by-detection model is only able to promote consistency between one frame and the next. In tracking-by-selection, path hypotheses exist across time, and encouraging long-term temporal consistency is as sim- ple as rewarding path hypotheses with consistent image fea- tures. One additional advantage of tracking-by-selection is that it results in a dramatically simplified model that can be solved exactly. We adapt an existing tracking-by-detection model to the tracking-by-selection framework, and show im- proved performance on a challenging dataset (introduced in [18]).
|
Similar papers:
[rank all papers by similarity to this]
|
|
To Aggregate or Not to aggregate: Selective Match Kernels for Image Search [pdf]
Giorgos Tolias, Yannis Avrithis, Herve Jegou |
|
Abstract: This paper considers a family of metrics to compare im- ages based on their local descriptors. It encompasses the VLAD descriptor and matching techniques such as Ham- ming Embedding. Making the bridge between these ap- proaches leads us to propose a match kernel that takes the best of existing techniques by combining an aggregation procedure with a selective match kernel. Finally, the rep- resentation underpinning this kernel is approximated, pro- viding a large scale image search both precise and scalable, as shown by our experiments on several benchmarks.
|
Similar papers:
[rank all papers by similarity to this]
|
|
BOLD Features to Detect Texture-less Objects [pdf]
Federico Tombari, Alessandro Franchi, Luigi Di_Stefano |
|
Abstract: Object detection in images withstanding significant clut- ter and occlusion is still a challenging task whenever the object surface is characterized by poor informative content. We propose to tackle this problem by a compact and dis- tinctive representation of groups of neighboring line seg- ments aggregated over limited spatial supports and invari- ant to rotation, translation and scale changes. Peculiarly, our proposal allows for leveraging on the inherent strengths of descriptor-based approaches, i.e. robustness to occlu- sion and clutter and scalability with respect to the size of the model library, also when dealing with scarcely textured objects.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: Over the last years, several authors have signaled that state of the art categorization methods fail to perform well when trained and tested on data from different databases. The general consensus in the literature is that this issue, known as domain adaptation and/or dataset bias, is due to a distribution mismatch between data collections. Meth- ods addressing it go from max-margin classifiers to learn- ing how to modify the features and obtain a more robust representation. The large majority of these works use BOW feature descriptors, and learning methods based on image- to-image distance functions.
Following the seminal work of [6], in this paper we chal- lenge these two assumptions. We experimentally show that using the NBNN classifier over existing domain adaptation databases achieves always very strong performances. We build on this result, and present an NBNN-based domain adaptation algorithm that learns iteratively a class metric while inducing, for each sample, a large margin separa- tion among classes. To the best of our knowledge, this is the first work casting the domain adaptation problem within the NBNN framework. Experiments show that our method achieves the state of the art, both in the unsupervised and semi-supervised settings.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: This paper investigates an approach for generating two grating images so that the moire pattern of their superpo- sition resembles the target image. Our method is grounded on the fundamental moire theorem. By focusing on the vi- sually most dominant (1, 1)-moire component, we obtain the phase modulation constraint on the phase shifts between the two grating images. For improving visual appearance of the grating images and hiding capability the embedded image, a smoothness term is added to spread information between the two grating images and an appearance phase function is used to add irregular structures into grating im- ages. The grating images can be printed on transparencies and the hidden image decoding can be performed optically by overlaying them together. The proposed method enables the creation of moire art and allows visual decoding with- out computers.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: When we look at an image, some properties or attributes of the image stand out more than others. When describing an image, people are likely to describe these dominant at- tributes first. Attribute dominance is a result of a complex interplay between the various properties present or absent in the image. Which attributes in an image are more domi- nant than others reveals rich information about the content of the image. In this paper we tap into this information by modeling attribute dominance. We show that this helps improve the performance of vision systems on a variety of human-centric applications such as zero-shot learning, im- age search and generating textual descriptions of images.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Detecting Irregular Curvilinear Structures in Gray Scale and Color Imagery Using Multi-directional Oriented Flux [pdf]
Engin Turetken, Carlos Becker, Przemyslaw Glowacki, Fethallah Benmansour, Pascal Fua |
|
Abstract: We propose a new approach to detecting irregular curvi- linear structures in noisy image stacks. In contrast to ear- lier approaches that rely on circular models of the cross- sections, ours allows for the arbitrarily-shaped ones that are prevalent in biological imagery. This is achieved by maximizing the image gradient flux along multiple direc- tions and radii, instead of only two with a unique radius as is usually done. This yields a more complex optimization problem for which we propose a computationally efficient solution.
We demonstrate the effectiveness of our approach on a wide range of challenging gray scale and color datasets and show that it outperforms existing techniques, especially on very irregular structures.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: We describe a very simple framework for deriving the most-well known optimization problems in Active Appear- ance Models (AAMs), and most importantly for providing efficient solutions. Our formulation results in two optimiza- tion problems for fast and exact AAM fitting, and one new algorithm which has the important advantage of being ap- plicable to 3D. We show that the dominant cost for both for- ward and inverse algorithms is a few times mN which is the cost of projecting an image onto the appearance subspace. This makes both algorithms not only computationally re- alizable but also very attractive speed-wise for most cur- rent systems. Because exact AAM fitting is no longer com- putationally prohibitive, we trained AAMs in-the-wild with the goal of investigating whether AAMs benefit from such a training process. Our results show that although we did not use sophisticated shape priors, robust features or robust norms for improving performance, AAMs perform notably well and in some cases comparably with current state-of- the-art methods. We provide Matlab source code for train- ing, fitting and reproducing the results presented in this pa- per at http://ibug.doc.ic.ac.uk/resources.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: This paper presents a probabilistic volumetric frame- work for image based modeling of general dynamic 3-d scenes. The framework is targeted towards high quality modeling of complex scenes evolving over thousands of frames. Extensive storage and computational resources are required in processing large scale space-time (4-d) data. Existing methods typically store separate 3-d models at each time step and do not address such limitations. A novel 4-d representation is proposed that adaptively subdivides in space and time to explain the appearance of 3-d dynamic surfaces. This representation is shown to achieve compres- sion of 4-d data and provide efficient spatio-temporal pro- cessing. The advances of the proposed framework is demon- strated on standard datasets using free-viewpoint video and 3-d tracking applications.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: 3D reconstruction deals with the problem of finding the shape of an object from a set of images. Thin objects that have virtually no volume pose a special challenge for recon- struction with respect to shape representation and fusion of depth information. In this paper we present a dense point- based reconstruction method that can deal with this special class of objects. We seek to jointly optimize a set of depth maps by treating each pixel as a point in space. Points are pulled towards a common surface by pairwise forces in an iterative scheme. The method also handles the problem of opposed surfaces by means of penalty forces. Efficient opti- mization is achieved by grouping points to superpixels and a spatial hashing approach for fast neighborhood queries. We show that the approach is on a par with state-of-the-art methods for standard multi view stereo settings and gives superior results for thin objects.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Compositional Models for Video Event Detection: A Multiple Kernel Learning Latent Variable Approach [pdf]
Arash Vahdat, Kevin Cannons, Greg Mori, Sangmin Oh, Ilseo Kim |
|
Abstract: We present a compositional model for video event detec- tion. A video is modeled using a collection of both global and segment-level features and kernel functions are em- ployed for similarity comparisons. The locations of salient, discriminative video segments are treated as a latent vari- able, allowing the model to explicitly ignore portions of the video that are unimportant for classification. A novel, mul- tiple kernel learning (MKL) latent support vector machine (SVM) is defined, that is used to combine and re-weight multiple feature types in a principled fashion while simul- taneously operating within the latent variable framework. The compositional nature of the proposed model allows it to respond directly to the challenges of temporal clutter and intra-class variation, which are prevalent in unconstrained internet videos. Experimental results on the TRECVID Mul- timedia Event Detection 2011 (MED11) dataset demon- strate the efficacy of the method.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: Gathering accurate training data for recognizing a set of attributes or tags on images or videos is a challenge. Ob- taining labels via manual effort or from weakly-supervised data typically results in noisy training labels. We develop the FlipSVM, a novel algorithm for handling these noisy, structured labels. The FlipSVM models label noise by flip- ping labels on training examples. We show empirically that the FlipSVM is effective on images-and-attributes and video tagging datasets.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Online Video SEEDS for Temporal Window Objectness [pdf]
Michael Van_Den_Bergh, Gemma Roig, Xavier Boix, Santiago Manen, Luc Van_Gool |
|
Abstract: Superpixel and objectness algorithms are broadly used as a pre-processing step to generate support regions and to speed-up further computations. Recently, many algo- rithms have been extended to video in order to exploit the temporal consistency between frames. However, most meth- ods are computationally too expensive for real-time appli- cations. We introduce an online, real-time video superpixel algorithm based on the recently proposed SEEDS superpix- els. A new capability is incorporated which delivers multi- ple diverse samples (hypotheses) of superpixels in the same image or video sequence. The multiple samples are shown to provide a strong cue to efficiently measure the object- ness of image windows, and we introduce the novel concept of objectness in temporal windows. Experiments show that the video superpixels achieve comparable performance to state-of-the-art offline methods while running at 30 fps on a single 2.8 GHz i7 CPU. State-of-the-art performance on objectness is also demonstrated, yet orders of magnitude faster and extended to temporal windows in video.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: Estimating dense 3D scene flow from stereo sequences remains a challenging task, despite much progress in both classical disparity and 2D optical flow estimation. To over- come the limitations of existing techniques, we introduce a novel model that represents the dynamic 3D scene by a collection of planar, rigidly moving, local segments. Scene flow estimation then amounts to jointly estimating the pixel- to-segment assignment, and the 3D position, normal vector, and rigid motion parameters of a plane for each segment. The proposed energy combines an occlusion-sensitive data term with appropriate shape, motion, and segmentation reg- ularizers. Optimization proceeds in two stages: Starting from an initial superpixelization, we estimate the shape and motion parameters of all segments by assigning a proposal from a set of moving planes. Then the pixel-to-segment as- signment is updated, while holding the shape and motion parameters of the moving planes fixed. We demonstrate the benefits of our model on different real-world image sets, including the challenging KITTI benchmark. We achieve leading performance levels, exceeding competing 3D scene flow methods, and even yielding better 2D motion estimates than all tested dedicated optical flow techniques.
|
Similar papers:
[rank all papers by similarity to this]
|
|
HOGgles: Visualizing Object Detection Features [pdf]
Carl Vondrick, Aditya Khosla, Tomasz Malisiewicz, Antonio Torralba |
|
Abstract: We introduce algorithms to visualize feature spaces used by object detectors. The tools in this paper allow a human to put on HOG goggles and perceive the visual world as a HOG based object detector sees it. We found that these visualizations allow us to analyze object detection systems in new ways and gain new insight into the detectors fail- ures. For example, when we visualize the features for high scoring false alarms, we discovered that, although they are clearly wrong in image space, they do look deceptively sim- ilar to true positives in feature space. This result suggests that many of these false alarms are caused by our choice of feature space, and indicates that creating a better learning algorithm or building bigger datasets is unlikely to correct these errors. By visualizing feature spaces, we can gain a more intuitive understanding of our detection systems.
|
Similar papers:
[rank all papers by similarity to this]
|
|
A Max-Margin Perspective on Sparse Representation-Based Classification [pdf]
Zhaowen Wang, Jianchao Yang, Nasser Nasrabadi, Thomas Huang |
|
Abstract: Sparse Representation-based Classification (SRC) is a powerful tool in distinguishing signal categories which lie on different subspaces. Despite its wide application to visu- al recognition tasks, current understanding of SRC is solely based on a reconstructive perspective, which neither offer- s any guarantee on its classification performance nor pro- vides any insight on how to design a discriminative dictio- nary for SRC. In this paper, we present a novel perspec- tive towards SRC and interpret it as a margin classifier. The decision boundary and margin of SRC are analyzed in local regions where the support of sparse code is stable. Based on the derived margin, we propose a hinge loss func- tion as the gauge for the classification performance of SRC. A stochastic gradient descent algorithm is implemented to maximize the margin of SRC and obtain more discrimina- tive dictionaries. Experiments validate the effectiveness of the proposed approach in predicting classification perfor- mance and improving dictionary quality over reconstructive ones. Classification results competitive with other state-of- the-art sparse coding methods are reported on several data sets.
|
Similar papers:
[rank all papers by similarity to this]
|
|
A Unified Probabilistic Approach Modeling Relationships between Attributes and Objects [pdf]
Xiaoyang Wang, Qiang Ji |
|
Abstract: This paper proposes a unified probabilistic model to model the relationships between attributes and objects for attribute prediction and object recognition. As a list of se- mantically meaningful properties of objects, attributes gen- erally relate to each other statistically. In this paper, we propose a unified probabilistic model to automatically dis- cover and capture both the object-dependent and object- independent attribute relationships. The model utilizes the captured relationships to benefit both attribute prediction and object recognition. Experiments on four benchmark attribute datasets demonstrate the effectiveness of the pro- posed unified model for improving attribute prediction as well as object recognition in both standard and zero-shot learning cases.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: Recently dense trajectories were shown to be an efficient video representation for action recognition and achieved state-of-the-art results on a variety of datasets. This pa- per improves their performance by taking into account cam- era motion to correct them. To estimate camera motion, we match feature points between frames using SURF descrip- tors and dense optical flow, which are shown to be com- plementary. These matches are, then, used to robustly es- timate a homography with RANSAC. Human motion is in general different from camera motion and generates incon- sistent matches. To improve the estimation, a human de- tector is employed to remove these matches. Given the es- timated camera motion, we remove trajectories consistent with it. We also use this estimation to cancel out camera motion from the optical flow. This significantly improves motion-based descriptors, such as HOF and MBH. Experi- mental results on four challenging action datasets (i.e., Hol- lywood2, HMDB51, Olympic Sports and UCF50) signifi- cantly outperform the current state of the art.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Bayesian Robust Matrix Factorization for Image and Video Processing [pdf]
Naiyan Wang, Dit-Yan Yeung |
|
Abstract: Matrix factorization is a fundamental problem that is often encountered in many computer vision and machine learning tasks. In recent years, enhancing the robustness of matrix factorization methods has attracted much attention in the research community. To benefit from the strengths of full Bayesian treatment over point estimation, we propose here a full Bayesian approach to robust matrix factoriza- tion. For the generative process, the model parameters have conjugate priors and the likelihood (or noise model) takes the form of a Laplace mixture. For Bayesian inference, we devise an efficient sampling algorithm by exploiting a hier- archical view of the Laplace distribution. Besides the basic model, we also propose an extension which assumes that the outliers exhibit spatial or temporal proximity as encoun- tered in many computer vision applications. The proposed methods give competitive experimental results when com- pared with several state-of-the-art methods on some bench- mark image and video processing tasks.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Capturing Global Semantic Relationships for Facial Action Unit Recognition [pdf]
Ziheng Wang, Yongqiang Li, Shangfei Wang, Qiang Ji |
|
Abstract: In this paper we tackle the problem of facial action unit (AU) recognition by exploiting the complex semantic rela- tionships among AUs, which carry crucial top-down infor- mation yet have not been thoroughly exploited. Towards this goal, we build a hierarchical model that combines the bottom-level image features and the top-level AU relation- ships to jointly recognize AUs in a principled manner. The proposed model has two major advantages over existing methods. 1) Unlike methods that can only capture local pair-wise AU dependencies, our model is developed upon the restricted Boltzmann machine and therefore can exploit the global relationships among AUs. 2) Although AU re- lationships are influenced by many related factors such as facial expressions, these factors are generally ignored by the current methods. Our model, however, can successfully capture them to more accurately characterize the AU rela- tionships. Efficient learning and inference algorithms of the proposed model are also developed. Experimental results on benchmark databases demonstrate the effectiveness of the proposed approach in modelling complex AU relation- ships as well as its superior AU recognition performance over existing approaches.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: One of the trends of action recognition consists in ex- tracting and comparing mid-level features which encode vi- sual and motion aspects of objects into scenes. However, when scenes contain high-level semantic actions with many interacting parts, these mid-level features are not sufficient to capture high level structures as well as high order causal relationships between moving objects resulting into a clear drop in performances.
In this paper, we address this issue and we propose an alternative action recognition method based on a nov- el graph kernel. In the main contributions of this work, we first describe actions in videos using directed acyclic graphs (DAGs), that naturally encode pairwise interactions between moving object parts, and then we compare these DAGs by analyzing the spectrum of their sub-patterns that capture complex higher order interactions. This extraction and comparison process is computationally tractable, re- sulting from the acyclic property of DAGs, and it also de- fines a positive semi-definite kernel. When plugging the latter into support vector machines, we obtain an action recognition algorithm that overtakes related work, includ- ing graph-based methods, on a standard evaluation dataset.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Dynamic Label Propagation for Semi-supervised Multi-class Multi-label Classification [pdf]
Bo Wang, Zhuowen Tu, John K. Tsotsos |
|
Abstract: In graph-based semi-supervised learning approaches, the classification rate is highly dependent on the size of the availabel labeled data, as well as the accuracy of the similarity measures. Here, we propose a semi-supervised multi-class/multi-label classification scheme, dynamic la- bel propagation (DLP), which performs transductive learn- ing through propagation in a dynamic process. Existing semi-supervised classification methods often have difficulty in dealing with multi-class/multi-label problems due to the lack in consideration of label correlation; our algorithm in- stead emphasizes dynamic metric fusion with label infor- mation. Significant improvement over the state-of-the-art methods is observed on benchmark datasets for both multi- class and multi-label tasks.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Fast Neighborhood Graph Search Using Cartesian Concatenation [pdf]
Jing Wang, Jingdong Wang, Gang Zeng, Rui Gan, Shipeng Li, Baining Guo |
|
Abstract: In this paper, we propose a new data structure for ap- proximate nearest neighbor search. This structure aug- ments the neighborhood graph with a bridge graph. We pro- pose to exploit Cartesian concatenation to produce a large set of vectors, called bridge vectors, from several small sets of subvectors. Each bridge vector is connected with a few reference vectors near to it, forming a bridge graph. Our approach finds nearest neighbors by simultaneously traversing the neighborhood graph and the bridge graph in the best-first strategy. The success of our approach stems from two factors: the exact nearest neighbor search over a large number of bridge vectors can be done quickly, and the reference vectors connected to a bridge (reference) vector near the query are also likely to be near the query. Experi- mental results on searching over large scale datasets (SIFT, GIST and HOG) show that our approach outperforms state- of-the-art ANN search algorithms in terms of efficiency and accuracy. The combination of our approach with the IV- FADC system [18] also shows superior performance over the BIGANN dataset of 1 billion SIFT features compared with the best previously published result.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Fast Subspace Search via Grassmannian Based Hashing [pdf]
Xu Wang, Stefan Atev, John Wright, Gilad Lerman |
|
Abstract: The problem of efficiently deciding which of a database of models is most similar to a given input query arises throughout modern computer vision. Motivated by appli- cations in recognition, image retrieval and optimization, there has been significant recent interest in the variant of this problem in which the database models are linear sub- spaces and the input is either a point or a subspace. Cur- rent approaches to this problem have poor scaling in high dimensions, and may not guarantee sublinear query com- plexity. We present a new approach to approximate near- est subspace search, based on a simple, new locality sen- sitive hash for subspaces. Our approach allows point-to- subspace query for a database of subspaces of arbitrary di- mension d, in a time that depends sublinearly on the num- ber of subspaces in the database. The query complexity of our algorithm is linear in the ambient dimension D, allow- ing it to be directly applied to high-dimensional imagery data. Numerical experiments on model problems in image repatching and automatic face recognition confirm the ad- vantages of our algorithm in terms of both speed and accu- racy.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Image Co-segmentation via Consistent Functional Maps [pdf]
Fan Wang, Qixing Huang, Leonidas J. Guibas |
|
Abstract: Joint segmentation of image sets has great importance for object recognition, image classification, and image re- trieval. In this paper, we aim to jointly segment a set of images starting from a small number of labeled images or none at all. To allow the images to share segmentation information with each other, we build a network that con- tains segmented as well as unsegmented images, and extract functional maps between connected image pairs based on image appearance features. These functional maps act as general property transporters between the images and, in particular, are used to transfer segmentations. We define and operate in a reduced functional space optimized so that the functional maps approximately satisfy cycle-consistency under composition in the network. A joint optimization framework is proposed to simultaneously generate all seg- mentation functions over the images so that they both align with local segmentation cues in each particular image, and agree with each other under network transportation. This formulation allows us to extract segmentations even with no training data, but can also exploit such data when available. The collective effect of the joint processing using functional maps leads to accurate information sharing among images and yields superior segmentation results, as shown on the iCoseg, MSRC, and PASCAL data sets.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: Graph matching has been widely used in various ap- plications in computer vision due to its powerful perfor- mance. However, it poses three challenges to image sparse feature matching: (1) The combinatorial nature limits the size of the possible matches; (2) It is sensitive to outliers because the objective function prefers more matches; (3) It works poorly when handling many-to-many object cor- respondences, due to its assumption of one single cluster for each graph. In this paper, we address these problems with a unified frameworkDensity Maximization. We pro- pose a graph density local estimator () to measure the quality of matches. Density Maximization aims to maxi- mize the values both locally and globally. The local maximization of finds the clusters of nodes as well as eliminates the outliers. The global maximization of efficiently refines the matches by exploring a much larger matching space. Our Density Maximization is orthogo- nal to specific graph matching algorithms. Experimental evaluation demonstrates that it significantly boosts the true matches and enables graph matching to handle both out- liers and many-to-many object correspondences.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Learning Coupled Feature Spaces for Cross-Modal Matching [pdf]
Kaiye Wang, Ran He, Wei Wang, Liang Wang, Tieniu Tan |
|
Abstract: Cross-modal matching has recently drawn much atten- tion due to the widespread existence of multimodal data. It aims to match data from different modalities, and generally involves two basic problems: the measure of relevance and coupled feature selection. Most previous works mainly fo- cus on solving the first problem. In this paper, we propose a novel coupled linear regression framework to deal with both problems. Our method learns two projection matrices to map multimodal data into a common feature space, in which cross-modal data matching can be performed. And in the learning procedure, the l21-norm penalties are imposed on the two projection matrices separately, which leads to s- elect relevant and discriminative features from coupled fea- ture spaces simultaneously. A trace norm is further imposed on the projected data as a low-rank constraint, which en- hances the relevance of different modal data with connec- tions. We also present an iterative algorithm based on half- quadratic minimization to solve the proposed regularized linear regression problem. The experimental results on two challenging cross-modal datasets demonstrate that the pro- posed method outperforms the state-of-the-art approaches.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: Hashing techniques have been intensively investigated in the design of highly efficient search engines for large- scale computer vision applications. Compared with prior approximate nearest neighbor search approaches like tree- based indexing, hashing-based search schemes have promi- nent advantages in terms of both storage and computational efficiencies. Moreover, the procedure of devising hash func- tions can be easily incorporated into sophisticated machine learning tools, leading to data-dependent and task-specific compact hash codes. Therefore, a number of learning paradigms, ranging from unsupervised to supervised, have been applied to compose appropriate hash functions. How- ever, most of the existing hash function learning methods either treat hash function design as a classification problem or generate binary codes to satisfy pairwise supervision, and have not yet directly optimized the search accuracy. In this paper, we propose to leverage listwise supervision into a principled hash function learning framework. In particu- lar, the ranking information is represented by a set of rank triplets that can be used to assess the quality of ranking. Simple linear projection-based hash functions are solved efficiently through maximizing the ranking quality over the training data. We carry out experiments on large image datasets with size up to one million and compare with the state-of-the-art hashing techniques. The extensive results corroborate that our learned hash codes v
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: Temporal misalignment and duration variation in video actions largely influence the performance of action recog- nition, but it is very difficult to specify effective temporal alignment on action sequences. To address this challenge, this paper proposes a novel discriminative learning-based temporal alignment method, called maximum margin tem- poral warping (MMTW), to align two action sequences and measure their matching score. Based on the latent struc- ture SVM formulation, the proposed MMTW method is able to learn a phantom action template to represent an action class for maximum discrimination against other classes. The recognition of this action class is based on the associ- ated learned alignment of the input action. Extensive exper- iments on five benchmark datasets have demonstrated that this MMTW model is able to significantly promote the ac- curacy and robustness of action recognition under temporal misalignment and variations.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Mining Motion Atoms and Phrases for Complex Action Recognition [pdf]
Limin Wang, Yu Qiao, Xiaoou Tang |
|
Abstract: This paper proposes motion atom and phrase as a mid- level temporal part for representing and classifying com- plex action. Motion atom is defined as an atomic part of action, and captures the motion information of action video in a short temporal scale. Motion phrase is a temporal composite of multiple motion atoms with an AND/OR struc- ture, which further enhances the discriminative ability of motion atoms by incorporating temporal constraints in a longer scale. Specifically, given a set of weakly labeled action videos, we firstly design a discriminative clustering method to automatically discover a set of representative mo- tion atoms. Then, based on these motion atoms, we mine ef- fective motion phrases with high discriminative and repre- sentative power. We introduce a bottom-up phrase construc- tion algorithm and a greedy selection method for this min- ing task. We examine the classification performance of the motion atom and phrase based representation on two com- plex action datasets: Olympic Sports and UCF50. Experi- mental results show that our method achieves superior per- formance over recent published methods on both datasets.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Online Robust Non-negative Dictionary Learning for Visual Tracking [pdf]
Naiyan Wang, Jingdong Wang, Dit-Yan Yeung |
|
Abstract: This paper studies the visual tracking problem in video sequences and presents a novel robust sparse tracker under the particle filter framework. In particular, we propose an online robust non-negative dictionary learning algorithm for updating the object templates so that each learned tem- plate can capture a distinctive aspect of the tracked object. Another appealing property of this approach is that it can automatically detect and reject the occlusion and cluttered background in a principled way. In addition, we propose a new particle representation formulation using the Huber loss function. The advantage is that it can yield robust esti- mation without using trivial templates adopted by previous sparse trackers, leading to faster computation. We also re- veal the equivalence between this new formulation and the previous one which uses trivial templates. The proposed tracker is empirically compared with state-of-the-art track- ers on some challenging video sequences. Both quantita- tive and qualitative comparisons show that our proposed tracker is superior and more stable.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: Generic object detection is confronted by dealing with different degrees of variations in distinct object classes with tractable computations, which demands for descriptive and flexible object representations that are also efficient to eval- uate for many locations. In view of this, we propose to model an object class by a cascaded boosting classifier which integrates various types of features from competing local regions, named as regionlets. A regionlet is a base feature extraction region defined proportionally to a detec- tion window at an arbitrary resolution (i.e. size and as- pect ratio). These regionlets are organized in small groups with stable relative positions to delineate fine-grained spa- tial layouts inside objects. Their features are aggregated to a one-dimensional feature within one group so as to tol- erate deformations. Then we evaluate the object bound- ing box proposal in selective search from segmentation cues, limiting the evaluation locations to thousands. Our approach significantly outperforms the state-of-the-art on popular multi-class detection benchmark datasets with a single method, without any contexts. It achieves the detec- tion mean average precision of 41.7% on the PASCAL VOC 2007 dataset and 39.7% on the VOC 2010 for 20 object cat- egories. It achieves 14.7% mean average precision on the ImageNet dataset for 200 object categories, outperforming the latest deformable part-based model (DPM) by 4.7%.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: This paper introduces to use semi-supervised learning for large scale image cosegmentation. Different from tra- ditional unsupervised cosegmentation that does not use any segmentation groundtruth, semi-supervised cosegmentation exploits the similarity from both the very limited training image foregrounds, as well as the common object shared be- tween the large number of unsegmented images. This would be a much practical way to effectively cosegment a large number of related images simultaneously, where previous unsupervised cosegmentation work poorly due to the large variances in appearance between different images and the lack of segmentation groundtruth for guidance in cosegmen- tation.
For semi-supervised cosegmentation in large scale, we propose an effective method by minimizing an energy func- tion, which consists of the inter-image distance, the intra- image distance and the balance term. We also propose an iterative updating algorithm to efficiently solve this energy function, which decomposes the original energy minimiza- tion problem into sub-problems, and updates each image alternatively to reduce the number of variables in each sub- problem for computation efficiency. Experiment results on iCoseg and Pascal VOC datasets show that the proposed cosegmentation method can effectively cosegment hundreds of images in less than one minute. And our semi-supervised cosegmentation is able to outperform both unsupervised cosegmentation as well as fully supervised single image
|
Similar papers:
[rank all papers by similarity to this]
|
|
Semi-supervised Robust Dictionary Learning via Efficient l-Norms Minimization [pdf]
Hua Wang, Feiping Nie, Weidong Cai, Heng Huang |
|
Abstract: Representing the raw input of a data set by a set of rele- vant codes is crucial to many computer vision applications. Due to the intrinsic sparse property of real-world data, dic- tionary learning, in which the linear decomposition of a data point uses a set of learned dictionary bases, i.e., codes, has demonstrated state-of-the-art performance. However, traditional dictionary learning methods suffer from three weaknesses: sensitivity to noisy and outlier samples, dif- ficulty to determine the optimal dictionary size, and incapa- bility to incorporate supervision information. In this paper, we address these weaknesses by learning a Semi-Supervised Robust Dictionary (SSR-D). Specifically, we use the l2,0+ - norm as the loss function to improve the robustness against outliers, and develop a new structured sparse regularization to incorporate the supervision information in dictionary learning, without incurring additional parameters. More- over, the optimal dictionary size is automatically learned from the input data. Minimizing the derived objective func- tion is challenging because it involves many non-smooth l2,0+ -norm terms. We present an efficient algorithm to solve the problem with a rigorous proof of the convergence of the algorithm. Extensive experiments are presented to show the superior performance of the proposed method.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Single-Patch Low-Rank Prior for Non-pointwise Impulse Noise Removal [pdf]
Ruixuan Wang, Emanuele Trucco |
|
Abstract: This paper introduces a low-rank prior for small ori- ented noise-free image patches: considering an oriented patch as a matrix, a low-rank matrix approximation is enough to preserve the texture details in the properly ori- ented patch. Based on this prior, we propose a single-patch method within a generalized joint low-rank and sparse matrix recovery framework to simultaneously detect and remove non-pointwise random-valued impulse noise (e.g., very small blobs). A weighting matrix is incorporated in the framework to encode an initial estimate of the spatial noise distribution. An accelerated proximal gradient method is adapted to estimate the optimal noise-free image patches. Experiments show the effectiveness of our framework in re- moving non-pointwise random-valued impulse noise.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Concurrent Action Detection with Structural Prediction [pdf]
Ping Wei, Nanning Zheng, Yibiao Zhao, Song-Chun Zhu |
|
Abstract: Action recognition has often been posed as a classifi- cation problem, which assumes that a video sequence only have one action class label and different actions are inde- pendent. However, a single human body can perform mul- tiple concurrent actions at the same time, and different ac- tions interact with each other. This paper proposes a con- current action detection model where the action detection is formulated as a structural prediction problem. In this model, an interval in a video sequence can be described by multiple action labels. An detected action interval is de- termined both by the unary local detector and the relations with other actions. We use a wavelet feature to represent the action sequence, and design a composite temporal logic descriptor to describe the action relations. The model pa- rameters are trained by structural SVM learning. Given a long video sequence, a sequential decision window search algorithm is designed to detect the actions. Experiments on our new collected concurrent action dataset demonstrate the strength of our method.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Modeling 4D Human-Object Interactions for Event and Object Recognition [pdf]
Ping Wei, Yibiao Zhao, Nanning Zheng, Song-Chun Zhu |
|
Abstract: Recognizing the events and objects in the video sequence are two challenging tasks due to the complex temporal structures and the large appearance variations. In this paper, we propose a 4D human-object interaction mod- el, where the two tasks jointly boost each other. Our human-object interaction is defined in 4D space: i) the co- occurrence and geometric constraints of human pose and object in 3D space; ii) the sub-events transition and object- s coherence in 1D temporal dimension. We represent the structure of events, sub-events and objects in a hierarchi- cal graph. For an input RGB-depth video, we design a dy- namic programming beam search algorithm to: i) segment the video, ii) recognize the events, and iii) detect the ob- jects simultaneously. For evaluation, we built a large-scale multiview 3D event dataset which contains 3815 video se- quences and 383,036 RGBD frames captured by the Kinect cameras. The experiment results on this dataset show the effectiveness of our method.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Multi-view Normal Field Integration for 3D Reconstruction of Mirroring Objects [pdf]
Michael Weinmann, Aljosa Osep, Roland Ruiters, Reinhard Klein |
|
Abstract: In this paper, we present a novel, robust multi-view nor- mal field integration technique for reconstructing the full 3D shape of mirroring objects. We employ a turntable- based setup with several cameras and displays. These are used to display illumination patterns which are reflected by the object surface. The pattern information observed in the cameras enables the calculation of individual volumet- ric normal fields for each combination of camera, display and turntable angle. As the pattern information might be blurred depending on the surface curvature or due to non- perfect mirroring surface characteristics, we locally adapt the decoding to the finest still resolvable pattern resolution. In complex real-world scenarios, the normal fields contain regions without observations due to occlusions and outliers due to interreflections and noise. Therefore, a robust re- construction using only normal information is challenging. Via a non-parametric clustering of normal hypotheses de- rived for each point in the scene, we obtain both the most likely local surface normal and a local surface consistency estimate. This information is utilized in an iterative min- cut based variational approach to reconstruct the surface geometry.
|
Similar papers:
[rank all papers by similarity to this]
|
|
DeepFlow: Large Displacement Optical Flow with Deep Matching [pdf]
Philippe Weinzaepfel, Jerome Revaud, Zaid Harchaoui, Cordelia Schmid |
|
Abstract: Optical flow computation is a key component in many computer vision systems designed for tasks such as action detection or activity recognition. However, despite several major advances over the last decade, handling large dis- placement in optical flow remains an open problem.
Inspired by the large displacement optical flow of Brox & Malik [6], our approach, termed DeepFlow, blends a matching algorithm with a variational approach for opti- cal flow. We propose a descriptor matching algorithm, tai- lored to the optical flow problem, that allows to boost per- formance on fast motions. The matching algorithm builds upon a multi-stage architecture with 6 layers, interleaving convolutions and max-pooling, a construction akin to deep convolutional nets. Using dense sampling, it allows to effi- ciently retrieve quasi-dense correspondences, and enjoys a built-in smoothing effect on descriptors matches, a valuable asset for integration into an energy minimization framework for optical flow estimation.
DeepFlow efficiently handles large displacements occur- ring in realistic videos, and shows competitive performance on optical flow benchmarks. Furthermore, it sets a new state-of-the-art on the MPI-Sintel dataset [8].
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: In many cases, the predictive power of structured models for for complex vision tasks is limited by a trade-off between the expressiveness and the computational tractability of the model. However, choosing this trade-off statically a priori is suboptimal, as images and videos in different settings vary tremendously in complexity. On the other hand, choosing the trade-off dynamically requires knowledge about the ac- curacy of different structured models on any given exam- ple. In this work, we propose a novel two-tier architecture that provides dynamic speed/accuracy trade-offs through a simple type of introspection. Our approach, which we call dynamic structured model selection (DMS), leverages typi- cally intractable features in structured learning problems in order to automatically determine which of several models should be used at test-time in order to maximize accuracy under a fixed budgetary constraint. We demonstrate DMS on two sequential modeling vision tasks, and we establish a new state-of-the-art in human pose estimation in video with an implementation that is roughly 23 faster than the pre- vious standard implementation.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Robust Feature Set Matching for Partial Face Recognition [pdf]
Renliang Weng, Jiwen Lu, Junlin Hu, Gao Yang, Yap-Peng Tan |
|
Abstract: Over the past two decades, a number of face recogni- tion methods have been proposed in the literature. Most of them use holistic face images to recognize people. However, human faces are easily occluded by other objects in many real-world scenarios and we have to recognize the person of interest from his/her partial faces. In this paper, we propose a new partial face recognition approach by using feature set matching, which is able to align partial face patches to holistic gallery faces automatically and is robust to occlu- sions and illumination changes. Given each gallery image and probe face patch, we first detect keypoints and extrac- t their local features. Then, we propose a Metric Learned Extended Robust Point Matching (MLERPM) method to dis- criminatively match local feature sets of a pair of gallery and probe samples. Lastly, the similarity of two faces is converted as the distance between two feature sets. Experi- mental results on three public face databases are presented to show the effectiveness of the proposed approach.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: Elongated objects have various shapes and can shift, ro- tate, change scale, and be rigid or deform by flexing, artic- ulating, and vibrating, with examples as varied as a glass bottle, a robotic arm, a surgical suture, a finger pair, a tram, and a guitar string. This generally makes tracking of poses of elongated objects very challenging.
We describe a unified, configurable framework for track- ing the pose of elongated objects, which move in the im- age plane and extend over the image region. Our method strives for simplicity, versatility, and efficiency. The object is decomposed into a chained assembly of segments of mul- tiple parts that are arranged under a hierarchy of tailored spatio-temporal constraints. In this hierarchy, segments can rescale independently while their elasticity is controlled with global orientations and local distances.
While the trend in tracking is to design complex, structure-free algorithms that update object appearance on- line, we show that our tracker, with the novel but remark- ably simple, structured organization of parts with constant appearance, reaches or improves state-of-the-art perfor- mance. Most importantly, our model can be easily config- ured to track exact pose of arbitrary, elongated objects in the image plane. The tracker can run up to 100 fps on a desktop PC, yet the computation time scales linearly with the number of object parts. To our knowledge, this is the first approach to generic tracking of elongated objects.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Discovering Details and Scene Structure with Hierarchical Iconoid Shift [pdf]
Tobias Weyand, Bastian Leibe |
|
Abstract: Current landmark recognition engines are typically aimed at recognizing building-scale landmarks, but miss in- teresting details like portals, statues or windows. This is because they use a flat clustering that summarizes all pho- tos of a building facade in one cluster. We propose Hier- archical Iconoid Shift, a novel landmark clustering algo- rithm capable of discovering such details. Instead of just a collection of clusters, the output of HIS is a set of dendro- grams describing the detail hierarchy of a landmark. HIS is based on the novel Hierarchical Medoid Shift clustering algorithm that performs a continuous mode search over the complete scale space. HMS is completely parameter-free, has the same complexity as Medoid Shift and is easy to par- allelize. We evaluate HIS on 800k images of 34 landmarks and show that it can extract an often surprising amount of detail and structure that can be applied, e.g., to provide a mobile user with more detailed information on a landmark or even to extend the landmarks Wikipedia article.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Network Principles for SfM: Disambiguating Repeated Structures with Local Context [pdf]
Kyle Wilson, Noah Snavely |
|
Abstract: Repeated features are common in urban scenes. Many objects, such as clock towers with nearly identical sides, or domes with strong radial symmetries, pose challenges for structure from motion. When similar but distinct features are mistakenly equated, the resulting 3D reconstructions can have errors ranging from phantom walls and superimposed structures to a complete failure to reconstruct. We present a new approach to solving such problems by considering the local visibility structure of such repeated features. Draw- ing upon network theory, we present a new way of scoring features using a measure of local clustering. Our model leads to a simple, fast, and highly scalable technique for disambiguating repeated features based on an analysis of an underlying visibility graph, without relying on explicit geometric reasoning. We demonstrate our method on several very large datasets drawn from Internet photo collections, and compare it to a more traditional geometry-based disam- biguation technique.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Cross-View Action Recognition over Heterogeneous Feature Spaces [pdf]
Xinxiao Wu, Han Wang, Cuiwei Liu, Yunde Jia |
|
Abstract: In cross-view action recognition, what you saw in one view is different from what you recognize in another view. The data distribution even the feature space can change from one view to another due to the appearance and motion of actions drastically vary across different views. In this pa- per, we address the problem of transferring action models learned in one view (source view) to another different view (target view), where action instances from these two views are represented by heterogeneous features. A novel learn- ing method, called Heterogeneous Transfer Discriminant- analysis of Canonical Correlations (HTDCC), is proposed to learn a discriminative common feature space for link- ing source and target views to transfer knowledge between them. Two projection matrices that respectively map data from source and target views into the common space are optimized via simultaneously minimizing the canonical cor- relations of inter-class samples and maximizing the intra- class canonical correlations. Our model is neither restrict- ed to corresponding action instances in the two views nor restricted to the same type of feature, and can handle on- ly a few or even no labeled samples available in the target view. To reduce the data distribution mismatch between the source and target views in the common feature space, a non- parametric criterion is included in the objective function. We additionally propose a joint weight learning method to fuse multiple source-view action class
|
Similar papers:
[rank all papers by similarity to this]
|
|
Learning Near-Optimal Cost-Sensitive Decision Policy for Object Detection [pdf]
Tianfu Wu, Song-Chun Zhu |
|
Abstract: Many object detectors, such as AdaBoost, SVM and de- formable part-based models (DPM), compute additive scor- ing functions at a large number of windows scanned over image pyramid, thus computational efficiency is an impor- tant consideration beside accuracy performance. In this paper, we present a framework of learning cost-sensitive decision policy which is a sequence of two-sided thresh- olds to execute early rejection or early acceptance based on the accumulative scores at each step. A decision policy is said to be optimal if it minimizes an empirical global risk function that sums over the loss of false negatives (FN) and false positives (FP), and the cost of computation. While the risk function is very complex due to high-order connections among the two-sided thresholds, we find its upper bound can be optimized by dynamic programming (DP) efficiently and thus say the learned policy is near-optimal. Given the loss of FN and FP and the cost in three numbers, our method can produce a policy on-the-fly for Adaboost, SVM and DPM. In experiments, we show that our decision policy outperforms state-of-the-art cascade methods significantly in terms of speed with similar accuracy performance.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: Detecting and registering nonrigid surfaces are two im- portant research problems for computer vision. Much work has been done with the assumption that there exists only one instance in the image. In this work, we propose an algorithm that detects and registers multiple nonrigid in- stances of given objects in a cluttered image. Specifically, after we use low level feature points to obtain the initial matches between templates and the input image, a novel high-order affinity graph is constructed to model the consis- tency of local topology. A hierarchical clustering approach is then used to locate the nonrigid surfaces. To remove the outliers in the cluster, we propose a deterministic anneal- ing approach based on the Thin Plate Spline (TPS) model. The proposed method achieves high accuracy even when the number of outliers is nineteen times larger than the inlier- s. As the matches may appear sparsely in each instance, we propose a TPS based match growing approach to prop- agate the matches. Finally, an approach that fuses feature and appearance information is proposed to register each nonrigid surface. Extensive experiments and evaluations demonstrate that the proposed algorithm achieves promis- ing results in detecting and registering multiple non-rigid surfaces in a cluttered scene.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Simultaneous Clustering and Tracklet Linking for Multi-face Tracking in Videos [pdf]
Baoyuan Wu, Siwei Lyu, Bao-Gang Hu, Qiang Ji |
|
Abstract: We describe a novel method that simultaneously clusters and associates short sequences of detected faces (termed as face tracklets) in videos. The rationale of our method is that face tracklet clustering and linking are related prob- lems that can benefit from the solutions of each other. Our method is based on a hidden Markov random field model that represents the joint dependencies of cluster labels and tracklet linking associations . We provide an efficient algo- rithm based on constrained clustering and optimal match- ing for the simultaneous inference of cluster labels and tracklet associations. We demonstrate significant improve- ments on the state-of-the-art results in face tracking and clustering performances on several video datasets.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: Inverted indexing is a popular non-exhaustive solution to large scale search. An inverted file is built by a quantizer such as k-means or a tree structure. It has been found that multiple inverted files, obtained by multiple independent random quantizers, are able to achieve practically good re- call and speed.
Instead of computing the multiple quantizers indepen- dently, we present a method that creates them jointly. Our method jointly optimizes all codewords in all quantizers. Then it assigns these codewords to the quantizers. In exper- iments this method shows significant improvement over var- ious existing methods that use multiple independent quan- tizers. On the one-billion set of SIFT vectors, our method is faster and more accurate than a recent state-of-the-art inverted indexing method.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Semantic Segmentation without Annotating Segments [pdf]
Wei Xia, Csaba Domokos, Jian Dong, Loong-Fah Cheong, Shuicheng Yan |
|
Abstract: Numerous existing object segmentation frameworks commonly utilize the object bounding box as a prior. In this paper, we address semantic segmentation assuming that ob- ject bounding boxes are provided by object detectors, but no training data with annotated segments are available. Based on a set of segment hypotheses, we introduce a simple vot- ing scheme to estimate shape guidance for each bound- ing box. The derived shape guidance is used in the sub- sequent graph-cut-based figure-ground segmentation. The final segmentation result is obtained by merging the seg- mentation results in the bounding boxes. We conduct an extensive analysis of the effect of object bounding box accu- racy. Comprehensive experiments on both the challenging PASCAL VOC object segmentation dataset and GrabCut- 50 image segmentation dataset show that the proposed ap- proach achieves competitive results compared to previous detection or bounding box prior based methods, as well as other state-of-the-art semantic segmentation methods.
|
Similar papers:
[rank all papers by similarity to this]
|
|
SUN3D: A Database of Big Spaces Reconstructed Using SfM and Object Labels [pdf]
Jianxiong Xiao, Andrew Owens, Antonio Torralba |
|
Abstract: Existing scene understanding datasets contain only a limited set of views of a place, and they lack representations of complete 3D spaces. In this paper, we introduce SUN3D, a large-scale RGB-D video database with camera pose and object labels, capturing the full 3D extent of many places. The tasks that go into constructing such a dataset are diffi- cult in isolation hand-labeling videos is painstaking, and structure from motion (SfM) is unreliable for large spaces. But if we combine them together, we make the dataset con- struction task much easier. First, we introduce an intuitive labeling tool that uses a partial reconstruction to propa- gate labels from one frame to another. Then we use the object labels to fix errors in the reconstruction. For this, we introduce a generalization of bundle adjustment that incor- porates object-to-object correspondences. This algorithm works by constraining points for the same object from dif- ferent frames to lie inside a fixed-size bounding box, pa- rameterized by its rotation and translation. The SUN3D database, the source code for the generalized bundle adjust- ment, and the web-based 3D annotation tool are all avail- able at http://sun3d.cs.princeton.edu.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Hierarchical Part Matching for Fine-Grained Visual Categorization [pdf]
Lingxi Xie, Qi Tian, Richang Hong, Shuicheng Yan, Bo Zhang |
|
Abstract: As a special topic in computer vision, fine-grained visual categorization (FGVC) has been attracting growing atten- tion these years. Different with traditional image classifi- cation tasks in which objects have large inter-class varia- tion, the visual concepts in the fine-grained datasets, such as hundreds of bird species, often have very similar seman- tics. Due to the large inter-class similarity, it is very difficult to classify the objects without locating really discriminative features, therefore it becomes more important for the algo- rithm to make full use of the part information in order to train a robust model.
In this paper, we propose a powerful flowchart named Hierarchical Part Matching (HPM) to cope with fine- grained classification tasks. We extend the Bag-of-Features (BoF) model by introducing several novel modules to inte- grate into image representation, including foreground in- ference and segmentation, Hierarchical Structure Learn- ing (HSL), and Geometric Phrase Pooling (GPP). We ver- ify in experiments that our algorithm achieves the state-of- the-art classification accuracy in the Caltech-UCSD-Birds- 200-2011 dataset by making full use of the ground-truth part annotations.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Inferring "Dark Matter" and "Dark Energy" from Videos [pdf]
Dan Xie, Sinisa Todorovic, Song-Chun Zhu |
|
Abstract: This paper presents an approach to localizing func- tional objects in surveillance videos without domain knowl- edge about semantic object classes that may appear in the scene. Functional objects do not have discriminative ap- pearance and shape, but they affect behavior of people in the scene. For example, they attract people to approach them for satisfying certain needs (e.g., vending machines could quench thirst), or repel people to avoid them (e.g., grass lawns). Therefore, functional objects can be viewed as dark matter, emanating dark energy that affects peoples trajectories in the video. To detect dark mat- ter and infer their dark energy field, we extend the La- grangian mechanics. People are treated as particle-agents with latent intents to approach dark matter and thus sat- isfy their needs, where their motions are subject to a com- posite dark energy field of all functional objects in the scene. We make the assumption that people take glob- ally optimal paths toward the intended dark matter while avoiding latent obstacles. A Bayesian framework is used to probabilistically model: peoples trajectories and intents, constraint map of the scene, and locations of functional ob- jects. A data-driven Markov Chain Monte Carlo (MCMC) process is used for inference. Our evaluation on videos of public squares and courtyards demonstrates our effective- ness in localizing functional objects and predicting peoples trajectories in unobserved parts of the video
|
Similar papers:
[rank all papers by similarity to this]
|
|
Parallel Transport of Deformations in Shape Space of Elastic Surfaces [pdf]
Qian Xie, Sebastian Kurtek, Huiling Le, Anuj Srivastava |
|
Abstract: Statistical shape analysis develops methods for com- parisons, deformations, summarizations, and modeling of shapes in given data sets. These tasks require a fundamental tool called parallel transport of tangent vectors along arbi- trary paths. This tool is essential for: (1) computation of geodesic paths using either shooting or path-straightening method, (2) transferring deformations across objects, and (3) modeling of statistical variability in shapes. Using the square-root normal field (SRNF) representation of parame- terized surfaces, we present a method for transporting de- formations along paths in the shape space. This is difficult despite the underlying space being a vector space because the chosen (elastic) Riemannian metric is non-standard. Using a finite-basis for representing SRNFs of shapes, we derive expressions for Christoffel symbols that enable par- allel transports. We demonstrate this framework using ex- amples from shape analysis of parameterized spherical sur- faces, in the three contexts mentioned above.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Robust Object Tracking with Online Multi-lifespan Dictionary Learning [pdf]
Junliang Xing, Jin Gao, Bing Li, Weiming Hu, Shuicheng Yan |
|
Abstract: Recently, sparse representation has been introduced for robust object tracking. By representing the object sparsely, i.e., using only a few templates via l1-norm minimization, these so-called l1-trackers exhibit promising tracking re- sults. In this work, we address the object template build- ing and updating problem in these l1-tracking approaches, which has not been fully studied. We propose to perform template updating, in a new perspective, as an online in- cremental dictionary learning problem, which is efficiently solved through an online optimization procedure. To guar- antee the robustness and adaptability of the tracking algo- rithm, we also propose to build a multi-lifespan dictionary model. By building target dictionaries of different lifespans, effective object observations can be obtained to deal with the well-known drifting problem in tracking and thus im- prove the tracking accuracy. We derive effective observa- tion models both generatively and discriminatively based on the online multi-lifespan dictionary learning model and de- ploy them to the Bayesian sequential estimation framework to perform tracking. The proposed approach has been ex- tensively evaluated on ten challenging video sequences. Ex- perimental results demonstrate the effectiveness of the on- line learned templates, as well as the state-of-the-art track- ing performance of the proposed approach.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: The archetype hull model is playing an important role in large-scale data analytics and mining, but rarely applied to vision problems. In this paper, we migrate such a geometric model to address face recognition and verification together through proposing a unified archetype hull ranking frame- work. Upon a scalable graph characterized by a compact set of archetype exemplars whose convex hull encompasses most of the training images, the proposed framework ex- plicitly captures the relevance between any query and the stored archetypes, yielding a rank vector over the archetype hull. The archetype hull ranking is then executed on ev- ery block of face images to generate a blockwise similarity measure that is achieved by comparing two different rank vectors with respect to the same archetype hull. After inte- grating blockwise similarity measurements with learned im- portance weights, we accomplish a sensible face similarity measure which can support robust and effective face recog- nition and verification. We evaluate the face similarity mea- sure in terms of experiments performed on three benchmark face databases Multi-PIE, Pubfig83, and LFW, demonstrat- ing its performance superior to the state-of-the-arts.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: We tackle the practical problem of hand pose estimation from a single noisy depth image. A dedicated three-step pipeline is proposed: Initial estimation step provides an initial estimation of the hand in-plane orientation and 3D location; Candidate generation step produces a set of 3D pose candidate from the Hough voting space with the help of the rotational invariant depth features; Verification step delivers the final 3D hand pose as the solution to an opti- mization problem. We analyze the depth noises, and sug- gest tips to minimize their negative impacts on the overall performance. Our approach is able to work with Kinect- type noisy depth images, and reliably produces pose esti- mations of general motions efficiently (12 frames per sec- ond). Extensive experiments are conducted to qualitatively and quantitatively evaluate the performance with respect to the state-of-the-art methods that have access to additional RGB images. Our approach is shown to deliver on par or even better results.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Feature Weighting via Optimal Thresholding for Video Analysis [pdf]
Zhongwen Xu, Yi Yang, Ivor Tsang, Nicu Sebe, Alexander G. Hauptmann |
|
Abstract: Fusion of multiple features can boost the performance of large-scale visual classification and detection tasks like TRECVID Multimedia Event Detection (MED) competi- tion [1]. In this paper, we propose a novel feature fusion approach, namely Feature Weighting via Optimal Thresh- olding (FWOT) to effectively fuse various features. FWOT learns the weights, thresholding and smoothing parame- ters in a joint framework to combine the decision values obtained from all the individual features and the early fu- sion. To the best of our knowledge, this is the first work to consider the weight and threshold factors of fusion prob- lem simultaneously. Compared to state-of-the-art fusion al- gorithms, our approach achieves promising improvements on HMDB [8] action recognition dataset and CCV [5] video classification dataset. In addition, experiments on two TRECVID MED 2011 collections show that our approach outperforms the state-of-the-art fusion methods for complex event detection.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Flattening Supervoxel Hierarchies by the Uniform Entropy Slice [pdf]
Chenliang Xu, Spencer Whitt, Jason J. Corso |
|
Abstract: Supervoxel hierarchies provide a rich multiscale decom- position of a given video suitable for subsequent processing in video analysis. The hierarchies are typically computed by an unsupervised process that is susceptible to under- segmentation at coarse levels and over-segmentation at fine levels, which make it a challenge to adopt the hierarchies for later use. In this paper, we propose the first method to overcome this limitation and flatten the hierarchy into a single segmentation. Our method, called the uniform en- tropy slice, seeks a selection of supervoxels that balances the relative level of information in the selected supervoxels based on some post hoc feature criterion such as object- ness. For example, with this criterion, in regions nearby objects, our method prefers finer supervoxels to capture the local details, but in regions away from any objects we pre- fer coarser supervoxels. We formulate the uniform entropy slice as a binary quadratic program and implement four dif- ferent feature criteria, both unsupervised and supervised, to drive the flattening. Although we apply it only to super- voxel hierarchies in this paper, our method is generally ap- plicable to segmentation tree hierarchies. Our experiments demonstrate both strong qualitative performance and supe- rior quantitative performance to state of the art baselines on benchmark internet videos.
|
Similar papers:
[rank all papers by similarity to this]
|
|
GOSUS: Grassmannian Online Subspace Updates with Structured-Sparsity [pdf]
Jia Xu, Vamsi K. Ithapu, Lopamudra Mukherjee, James M. Rehg, Vikas Singh |
|
Abstract: We study the problem of online subspace learning in the context of sequential observations involving structured per- turbations. In online subspace learning, the observations are an unknown mixture of two components presented to the model sequentially the main effect which pertains to the subspace and a residual/error term. If no additional re- quirement is imposed on the residual, it often corresponds to noise terms in the signal which were unaccounted for by the main effect. To remedy this, one may impose struc- tural contiguity, which has the intended effect of leverag- ing the secondary terms as a covariate that helps the esti- mation of the subspace itself, instead of merely serving as a noise residual. We show that the corresponding online estimation procedure can be written as an approximate op- timization process on a Grassmannian. We propose an ef- ficient numerical solution, GOSUS, Grassmannian Online Subspace Updates with Structured-sparsity, for this prob- lem. GOSUS is expressive enough in modeling both homo- geneous perturbations of the subspace and structural conti- guities of outliers, and after certain manipulations, solvable via an alternating direction method of multipliers (ADMM). We evaluate the empirical performance of this algorithm on two problems of interest: online background subtraction and online multiple face tracking, and demonstrate that it achieves competitive performance with the state-of-the-art in near real time.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Human Re-identification by Matching Compositional Template with Cluster Sampling [pdf]
Yuanlu Xu, Liang Lin, Wei-Shi Zheng, Xiaobai Liu |
|
Abstract: This paper aims at a newly raising task in visual surveil- lance: re-identifying people at a distance by matching body information, given several reference examples. Most of ex- isting works solve this task by matching a reference tem- plate with the target individual, but often suffer from large human appearance variability (e.g. different poses/views, illumination) and high false positives in matching caused by conjunctions, occlusions or surrounding clutters. Address- ing these problems, we construct a simple yet expressive template from a few reference images of a certain individ- ual, which represents the body as an articulated assembly of compositional and alternative parts, and propose an effec- tive matching algorithm with cluster sampling. This algo- rithm is designed within a candidacy graph whose vertices are matching candidates (i.e. a pair of source and target body parts), and iterates in two steps for convergence. (i) It generates possible partial matches based on compatible and competitive relations among body parts. (ii) It con- firms the partial matches to generate a new matching solu- tion, which is accepted by the Markov Chain Monte Carlo (MCMC) mechanism. In the experiments, we demonstrate the superior performance of our approach on three public databases compared to existing methods.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: Data sparsity has been a thorny issue for manifold-based image synthesis, and in this paper we address this critical problem by leveraging ideas from transfer learning. Specif- ically, we propose methods based on generating auxiliary data in the form of synthetic samples using transformations of the original sparse samples. To incorporate the auxiliary data, we propose a weighted data synthesis method, which adaptively selects from the generated samples for inclusion during the manifold learning process via a weighted iter- ative algorithm. To demonstrate the feasibility of the pro- posed method, we apply it to the problem of face image syn- thesis from sparse samples. Compared with existing meth- ods, the proposed method shows encouraging results with good performance improvements.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Perceptual Fidelity Aware Mean Squared Error [pdf]
Wufeng Xue, Xuanqin Mou, Lei Zhang, Xiangchu Feng |
|
Abstract: How to measure the perceptual quality of natural images is an important problem in low level vision. It is known that the Mean Squared Error (MSE) is not an effective index to describe the perceptual fidelity of images. Numerous per- ceptual fidelity indices have been developed, while the rep- resentatives include the Structural SIMilarity (SSIM) index and its variants. However, most of those perceptual mea- sures are nonlinear, and they cannot be easily adopted as an objective function to minimize in various low level vision tasks. Can MSE be perceptual fidelity aware after some mi- nor adaptation?
In this paper we propose a simple framework to enhance the perceptual fidelity awareness of MSE by introducing an l2-norm structural error term to it. Such a Structural MSE (SMSE) can lead to very competitive image quality assess- ment (IQA) results. More surprisingly, we show that by us- ing certain structure extractors, SMSE can be further turned into a Gaussian smoothed MSE (i.e., the Euclidean distance between the original and distorted images after Gaussian smooth filtering), which is much simpler to calculate but achieves rather better IQA performance than SSIM. The so- called Perceptual-fidelity Aware MSE (PAMSE) can have great potentials in applications such as perceptual image coding and perceptual image restoration.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: When a translucent liquid is spilled over a rough surface it causes a significant change in the visual appearance of the surface. This wetting phenomenon is easily detected by humans, and an early model was devised by the physicist Andres Jonas Angstrom nearly a century ago. In this pa- per we investigate the problem of determining if a wet/dry relationship between two image patches explains the differ- ences in their visual appearance. Water tends to be the typ- ical liquid involved and therefore it is the main objective. At the same time, we consider the general problem where the liquid has some of the characteristics of water (i.e., a similar refractive index), but has an unknown spectral ab- sorption profile (e.g., coffee, tea, wine, etc.). We report on several experiments using our own images, a publicly avail- able dataset, and images downloaded from the web.
1. Background
When a material absorbs a liquid it changes visual ap- pearance due to richer light reflection and refraction pro- cesses. Humans easily detect wet versus dry surfaces, and are capable of integrating this ability in object detection and segmentation. As a result, a wet part of a surface is associ- ated with the dry part of the same surface despite significant differences in their appearance. For example, when driving over a partially wet road surface it is easily recognized as a drivable surface. Similarly, a wine spill on a couch is rec- ognized as a stain and not a separate object. The same ca- pabi
|
Similar papers:
[rank all papers by similarity to this]
|
|
Paper Doll Parsing: Retrieving Similar Styles to Parse Clothing Items [pdf]
Kota Yamaguchi, M. Hadi Kiapour, Tamara L. Berg |
|
Abstract: Clothing recognition is an extremely challenging prob- lem due to wide variation in clothing item appearance, layering, and style. In this paper, we tackle the clothing parsing problem using a retrieval based approach. For a query image, we find similar styles from a large database of tagged fashion images and use these examples to parse the query. Our approach combines parsing from: pre-trained global clothing models, local clothing models learned on the fly from retrieved examples, and transferred parse masks (paper doll item transfer) from retrieved examples. Exper- imental evaluation shows that our approach significantly outperforms state of the art in parsing accuracy.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Cross-Field Joint Image Restoration via Scale Map [pdf]
Qiong Yan, Xiaoyong Shen, Li Xu, Shaojie Zhuo, Xiaopeng Zhang, Liang Shen, Jiaya Jia |
|
Abstract: Color, infrared, and flash images captured in different fields can be employed to effectively eliminate noise and other visual artifacts. We propose a two-image restoration framework considering input images in different fields, for example, one noisy color image and one dark-flashed near- infrared image. The major issue in such a framework is to handle structure divergence and find commonly usable edges and smooth transition for visually compelling image reconstruction. We introduce a scale map as a competent representation to explicitly model derivative-level confi- dence and propose new functions and a numerical solver to effectively infer it following new structural observations. Our method is general and shows a principled way for cross-field restoration.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Joint Optimization for Consistent Multiple Graph Matching [pdf]
Junchi Yan, Yu Tian, Hongyuan Zha, Xiaokang Yang, Ya Zhang, Stephen M. Chu |
|
Abstract: The problem of graph matching in general is NP-hard and approaches have been proposed for its suboptimal so- lution, most focusing on finding the one-to-one node map- ping between two graphs. A more general and challenging problem arises when one aims to find consistent mappings across a number of graphs more than two. Conventional graph pair matching methods often result in mapping in- consistency since the mapping between two graphs can ei- ther be determined by pair mapping or by an additional anchor graph. To address this issue, a novel formulation is derived which is maximized via alternating optimization. Our method enjoys several advantages: 1) the mappings are jointly optimized rather than sequentially performed by ap- plying pair matching, allowing the global affinity informa- tion across graphs can be propagated and explored; 2) the number of concerned variables to optimize is in linear with the number of graphs, being superior to local pair match- ing resulting in O(n2) variables; 3) the mapping consis- tency constraints are analytically satisfied during optimiza- tion; and 4) off-the-shelf graph pair matching solvers can be reused under the proposed framework in an out-of-the- box fashion. Competitive results on both the synthesized data and the real data are reported, by varying the level of deformation, outliers and edge densities.
Corresponding author. The work is supported by NSF IIS- 1116886, NSF IIS-1049694, NSFC 61129001/F010403 and the 111 Project (B07
|
Similar papers:
[rank all papers by similarity to this]
|
|
No Matter Where You Are: Flexible Graph-Guided Multi-task Learning for Multi-view Head Pose Classification under Target Motion [pdf]
Yan Yan, Elisa Ricci, Ramanathan Subramanian, Oswald Lanz, Nicu Sebe |
|
Abstract: We propose a novel Multi-Task Learning framework (FEGA-MTL) for classifying the head pose of a person who moves freely in an environment monitored by multi- ple, large field-of-view surveillance cameras. As the tar- get (person) moves, distortions in facial appearance ow- ing to camera perspective and scale severely impede per- formance of traditional head pose classification methods. FEGA-MTL operates on a dense uniform spatial grid and learns appearance relationships across partitions as well as partition-specific appearance variations for a given head pose to build region-specific classifiers. Guided by two graphs which a-priori model appearance similarity among (i) grid partitions based on camera geometry and (ii) head pose classes, the learner efficiently clusters appearance- wise related grid partitions to derive the optimal partition- ing. For pose classification, upon determining the targets position using a person tracker, the appropriate region- specific classifier is invoked. Experiments confirm that FEGA-MTL achieves state-of-the-art classification with few training data.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Complex 3D General Object Reconstruction from Line Drawings [pdf]
Linjie Yang, Jianzhuang Liu, Xiaoou Tang |
|
Abstract: An important topic in computer vision is 3D object reconstruction from line drawings. Previous algorithms either deal with simple general objects or are limited to only manifolds (a subset of solids). In this paper, we propose a novel approach to 3D reconstruction of complex general objects, including manifolds, non-manifold solids, and non-solids. Through developing some 3D object properties, we use the degree of freedom of objects to decompose a complex line drawing into multiple simpler line drawings that represent meaningful building blocks of a complex object. After 3D objects are reconstructed from the decomposed line drawings, they are merged to form a complex object from their touching faces, edges, and vertices. Our experiments show a number of reconstruction examples from both complex line drawings and images with line drawings superimposed. Comparisons are also given to indicate that our algorithm can deal with much more complex line drawings of general objects than previous algorithms.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: We present a hybrid parametric and nonparametric al- gorithm, exemplar cut, for generating class-specific object segmentation hypotheses. For the parametric part, we train a pylon model on a hierarchical region tree as the energy function for segmentation. For the nonparametric part, we match the input image with each exemplar by using regions to obtain a score which augments the energy function from the pylon model. Our method thus generates a set of highly plausible segmentation hypotheses by solving a series of ex- emplar augmented graph cuts. Experimental results on the Graz and PASCAL datasets show that the proposed algo- rithm achieves favorable segmentation performance against the state-of-the-art methods in terms of visual quality and accuracy.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: The goal of single-image super-resolution is to gener- ate a high-quality high-resolution image based on a given low-resolution input. It is an ill-posed problem which re- quires exemplars or priors to better reconstruct the missing high-resolution image details. In this paper, we propose to split the feature space into numerous subspaces and col- lect exemplars to learn priors for each subspace, thereby creating effective mapping functions. The use of split in- put space facilitates both feasibility of using simple func- tions for super-resolution, and efficiency of generating high- resolution results. High-quality high-resolution images are reconstructed based on the effective learned priors. Experi- mental results demonstrate that the proposed algorithm per- forms efficiently and effectively over state-of-the-art meth- ods.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Go-ICP: Solving 3D Registration Efficiently and Globally Optimally [pdf]
Jiaolong Yang, Hongdong Li, Yunde Jia |
|
Abstract: Registration is a fundamental task in computer vision. The Iterative Closest Point (ICP) algorithm is one of the widely-used methods for solving the registration problem. Based on local iteration, ICP is however well-known to suffer from local minima. Its performance critically relies on the quality of initialization, and only local optimality is guaranteed. This paper provides the very first globally op- timal solution to Euclidean registration of two 3D pointsets or two 3D surfaces under the L2 error. Our method is built upon ICP, but combines it with a branch-and-bound (BnB) scheme which searches the 3D motion space SE(3) effi- ciently. By exploiting the special structure of the underlying geometry, we derive novel upper and lower bounds for the ICP error function. The integration of local ICP and global BnB enables the new method to run efficiently in practice, and its optimality is exactly guaranteed. We also discuss extensions, addressing the issue of outlier robustness.
|
Similar papers:
[rank all papers by similarity to this]
|
|
How Related Exemplars Help Complex Event Detection in Web Videos? [pdf]
Yi Yang, Zhigang Ma, Zhongwen Xu, Shuicheng Yan, Alexander G. Hauptmann |
|
Abstract: Compared to visual concepts such as actions, scenes and objects, complex event is a higher level abstraction of longer video sequences. For example, a marriage pro- posal event is described by multiple objects (e.g., ring, faces), scenes (e.g., in a restaurant, outdoor) and actions (e.g., kneeling down). The positive exemplars which exactly convey the precise semantic of an event are hard to obtain. It would be beneficial to utilize the related exemplars for complex event detection. However, the semantic correla- tions between related exemplars and the target event vary substantially as relatedness assessment is subjective. Two related exemplars can be about completely different events, e.g., in the TRECVID MED dataset, both bicycle riding and equestrianism are labeled as related to attempting a bike trick event. To tackle the subjectiveness of human as- sessment, our algorithm automatically evaluates how pos- itive the related exemplars are for the detection of an event and uses them on an exemplar-specific basis. Experiments demonstrate that our algorithm is able to utilize related ex- emplars adaptively, and the algorithm gains good perform- ance for complex event detection.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Modeling Self-Occlusions in Dynamic Shape and Appearance Tracking [pdf]
Yanchao Yang, Ganesh Sundaramoorthi |
|
Abstract: We present a method to track the precise shape of a dy- namic object in video. Joint dynamic shape and appear- ance models, in which a template of the object is propa- gated to match the object shape and radiance in the next frame, are advantageous over methods employing global image statistics in cases of complex object radiance and cluttered background. In cases of complex 3D object mo- tion and relative viewpoint change, self-occlusions and dis- occlusions of the object are prominent, and current meth- ods employing joint shape and appearance models are un- able to accurately adapt to new shape and appearance in- formation, leading to inaccurate shape detection. In this work, we model self-occlusions and dis-occlusions in a joint shape and appearance tracking framework. Experiments on video exhibiting occlusion/dis-occlusion, complex radiance and background show that occlusion/dis-occlusion model- ing leads to superior shape accuracy compared to recent methods employing joint shape/appearance models or em- ploying global statistics.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Sieving Regression Forest Votes for Facial Feature Detection in the Wild [pdf]
Heng Yang, Ioannis Patras |
|
Abstract: In this paper we propose a method for the localization of multiple facial features on challenging face images. In the regression forests (RF) framework, observations (patches) that are extracted at several image locations cast votes for the localization of several facial features. In order to fil- ter out votes that are not relevant, we pass them through two types of sieves, that are organised in a cascade, and which enforce geometric constraints. The first sieve filters out votes that are not consistent with a hypothesis for the location of the face center. Several sieves of the second type, one associated with each individual facial point, fil- ter out distant votes. We propose a method that adjusts on- the-fly the proximity threshold of each second type sieve by applying a classifier which, based on middle-level features extracted from voting maps for the facial feature in ques- tion, makes a sequence of decisions on whether the thresh- old should be reduced or not. We validate our proposed method on two challenging datasets with images collected from the Internet in which we obtain state of the art results without resorting to explicit facial shape models. We also show the benefits of our method for proximity threshold ad- justment especially on difficult face images.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Sparse Variation Dictionary Learning for Face Recognition with a Single Training Sample per Person [pdf]
Meng Yang, Luc Van_Gool, Lei Zhang |
|
Abstract: Face recognition (FR) with a single training sample per person (STSPP) is a very challenging problem due to the lack of information to predict the variations in the query sample. Sparse representation based classification has shown interesting results in robust FR; however, its perfor- mance will deteriorate much for FR with STSPP. To address this issue, in this paper we learn a sparse variation dic- tionary from a generic training set to improve the query sample representation by STSPP. Instead of learning from the generic training set independently w.r.t. the gallery set, the proposed sparse variation dictionary learning (SVDL) method is adaptive to the gallery set by jointly learning a projection to connect the generic training set with the gallery set. The learnt sparse variation dictionary can be easily integrated into the framework of sparse representa- tion based classification so that various variations in face images, including illumination, expression, occlusion, pose, etc., can be better handled. Experiments on the large-scale CMU Multi-PIE, FRGC and LFW databases demonstrate the promising performance of SVDL on FR with STSPP.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: Object functionality refers to the quality of an object that allows humans to perform some specific actions. It has been shown in psychology that functionality (affordance) is at least as essential as appearance in object recogni- tion by humans. In computer vision, most previous work on functionality either assumes exactly one functionality for each object, or requires detailed annotation of human pos- es and objects. In this paper, we propose a weakly super- vised approach to discover all possible object functionali- ties. Each object functionality is represented by a specific type of human-object interaction. Our method takes any possible human-object interaction into consideration, and evaluates image similarity in 3D rather than 2D in order to cluster human-object interactions more coherently. Ex- perimental results on a dataset of people interacting with musical instruments show the effectiveness of our approach.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: Traditional stereo matching assumes perspective view- ing cameras under a translational motion: the second cam- era is translated away from the first one to create parallax. In this paper, we investigate a different, rotational stere- o model on a special multi-perspective camera, the XSlit camera [9, 24]. We show that rotational XSlit (R-XSlit) stereo can be effectively created by fixing the sensor and slit locations but switching the two slits directions. We first derive the epipolar geometry of R-XSlit in the 4D light field ray space. Our derivation leads to a simple but effective scheme for locating corresponding epipolar curves. To conduct stereo matching, we further derive a new disparity term in our model and develop a patch-based graph-cut so- lution. To validate our theory, we assemble an XSlit lens by using a pair of cylindrical lenses coupled with slit-shaped apertures. The XSlit lens can be mounted on commodity cameras where the slit directions are adjustable to form desirable R-XSlit pairs. We show through experiments that R-XSlit provides a potentially advantageous imaging system for conducting fixed-location, dynamic baseline stereo.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Large-Scale Video Hashing via Structure Learning [pdf]
Guangnan Ye, Dong Liu, Jun Wang, Shih-Fu Chang |
|
Abstract: Recently, learning based hashing methods have become popular for indexing large-scale media data. Hashing methods map high-dimensional features to compact bina- ry codes that are efficient to match and robust in preserving original similarity. However, most of the existing hashing methods treat videos as a simple aggregation of indepen- dent frames and index each video through combining the indexes of frames. The structure information of videos, e.g., discriminative local visual commonality and temporal con- sistency, is often neglected in the design of hash functions. In this paper, we propose a supervised method that explores the structure learning techniques to design efficient hash functions. The proposed video hashing method formulates a minimization problem over a structure-regularized empir- ical loss. In particular, the structure regularization exploits the common local visual patterns occurring in video frames that are associated with the same semantic class, and si- multaneously preserves the temporal consistency over suc- cessive frames from the same video. We show that the min- imization objective can be efficiently solved by an Acceler- ated Proximal Gradient (APG) method. Extensive experi- ments on two large video benchmark datasets (up to around 150K video clips with over 12 million frames) show that the proposed method significantly outperforms the state-of- the-art hashing methods.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Initialization-Insensitive Visual Tracking through Voting with Salient Local Features [pdf]
Kwang Moo Yi, Hawook Jeong, Byeongho Heo, Hyung Jin Chang, Jin Young Choi |
|
Abstract: In this paper we propose an object tracking method in case of inaccurate initializations. To track objects accu- rately in such situation, the proposed method uses motion saliency and descriptor saliency of local features and performs tracking based on generalized Hough transform (GHT). The proposed motion saliency of a local feature em- phasizes features having distinctive motions, compared to the motions which are not from the target object. The de- scriptor saliency emphasizes features which are likely to be of the object in terms of its feature descriptors. Through these saliencies, the proposed method tries to learn and find the target object rather than looking for what was given at initialization, giving robust results even with inac- curate initializations. Also, our tracking result is obtained by combining the results of each local feature of the tar- get and the surroundings with GHT voting, thus is robust against severe occlusions as well. The proposed method is compared against nine other methods, with nine image sequences, and hundred random initializations. The exper- imental results show that our method outperforms all other compared methods.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Line Assisted Light Field Triangulation and Stereo Matching [pdf]
Zhan Yu, Xinqing Guo, Haibing Lin, Andrew Lumsdaine, Jingyi Yu |
|
Abstract: Light fields are image-based representations that use densely sampled rays as a scene description. In this paper, we explore geometric structures of 3D lines in ray space for improving light field triangulation and stereo match- ing. The triangulation problem aims to fill in the ray space with continuous and non-overlapping simplices anchored at sampled points (rays). Such a triangulation provides a piecewise-linear interpolant useful for light field super- resolution. We show that the light field space is largely bi- linear due to 3D line segments in the scene, and direct tri- angulation of these bilinear subspaces leads to large errors. We instead present a simple but effective algorithm to first map bilinear subspaces to line constraints and then apply Constrained Delaunay Triangulation (CDT). Based on our analysis, we further develop a novel line-assisted graph- cut (LAGC) algorithm that effectively encodes 3D line con- straints into light field stereo matching. Experiments on synthetic and real data show that both our triangulation and LAGC algorithms outperform state-of-the-art solutions in accuracy and visual quality.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Pose-Free Facial Landmark Fitting via Optimized Part Mixtures and Cascaded Deformable Shape Model [pdf]
Xiang Yu, Junzhou Huang, Shaoting Zhang, Wang Yan, Dimitris N. Metaxas |
|
Abstract: This paper addresses the problem of facial landmark lo- calization and tracking from a single camera. We present a two-stage cascaded deformable shape model to effective- ly and efficiently localize facial landmarks with large head pose variations. For face detection, we propose a group sparse learning method to automatically select the most salient facial landmarks. By introducing 3D face shape model, we use procrustes analysis to achieve pose-free fa- cial landmark initialization. For deformation, the first step uses mean-shift local search with constrained local mod- el to rapidly approach the global optimum. The second step uses component-wise active contours to discriminatively re- fine the subtle shape variation. Our framework can simul- taneously handle face detection, pose-free landmark local- ization and tracking in real time. Extensive experiments are conducted on both laboratory environmental face databas- es and face-in-the-wild databases. All results demonstrate that our approach has certain advantages over state-of-the- art methods in handling pose variations1.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Learning Slow Features for Behaviour Analysis [pdf]
Lazaros Zafeiriou, Mihalis A. Nicolaou, Stefanos Zafeiriou, Symeon Nikitidis, Maja Pantic |
|
Abstract: A recently introduced latent feature learning technique for time varying dynamic phenomena analysis is the so- called Slow Feature Analysis (SFA). SFA is a determinis- tic component analysis technique for multi-dimensional se- quences that by minimizing the variance of the first order time derivative approximation of the input signal finds un- correlated projections that extract slowly-varying features ordered by their temporal consistency and constancy. In this paper, we propose a number of extensions in both the deterministic and the probabilistic SFA optimization frame- works. In particular, we derive a novel deterministic SFA algorithm that is able to identify linear projections that ex- tract the common slowest varying features of two or more sequences. In addition, we propose an Expectation Max- imization (EM) algorithm to perform inference in a prob- abilistic formulation of SFA and similarly extend it in or- der to handle two and more time varying data sequences. Moreover, we demonstrate that the probabilistic SFA (EM- SFA) algorithm that discovers the common slowest varying latent space of multiple sequences can be combined with dynamic time warping techniques for robust sequence time- alignment. The proposed SFA algorithms were applied for facial behavior analysis demonstrating their usefulness and appropriateness for this task.
|
Similar papers:
[rank all papers by similarity to this]
|
|
On One-Shot Similarity Kernels: Explicit Feature Maps and Properties [pdf]
Stefanos Zafeiriou, Irene Kotsia |
|
Abstract: Kernels have been a common tool of machine learn- ing and computer vision applications for modeling non- linearities and/or the design of robust1 similarity measures between objects. Arguably, the class of positive semi- definite (psd) kernels, widely known as Mercers Kernels, constitutes one of the most well-studied cases. For every psd kernel there exists an associated feature map to an ar- bitrary dimensional Hilbert space H, the so-called feature space. The main reason behind psd kernels popularity is the fact that classification/regression techniques (such as Support Vector Machines (SVMs)) and component analy- sis algorithms (such as Kernel Principal Component Analy- sis (KPCA)) can be devised in H, without an explicit def- inition of the feature map, only by using the kernel (the so-called kernel trick). Recently, due to the development of very efficient solutions for large scale linear SVMs and for incremental linear component analysis, the research to- wards finding feature map approximations for classes of kernels has attracted significant interest. In this paper, we attempt the derivation of explicit feature maps of a recently proposed class of kernels, the so-called one-shot similarity kernels. We show that for this class of kernels either there exists an explicit representation in feature space or the ker- nel can be expressed in such a form that allows for exact in- cremental learning. We theoretically explore the properties of these kernels and show how these
|
Similar papers:
[rank all papers by similarity to this]
|
|
The Moving Pose: An Efficient 3D Kinematics Descriptor for Low-Latency Action Recognition and Detection [pdf]
Mihai Zanfir, Marius Leordeanu, Cristian Sminchisescu |
|
Abstract: Human action recognition under low observational la- tency is receiving a growing interest in computer vision due to rapidly developing technologies in human-robot interac- tion, computer gaming and surveillance. In this paper we propose a fast, simple, yet powerful non-parametric Mov- ing Pose (MP) framework for low-latency human action and activity recognition. Central to our methodology is a mov- ing pose descriptor that considers both pose information as well as differential quantities (speed and acceleration) of the human body joints within a short time window around the current frame. The proposed descriptor is used in con- junction with a modified kNN classifier that considers both the temporal location of a particular frame within the ac- tion sequence as well as the discrimination power of its moving pose descriptor compared to other frames in the training set. The resulting method is non-parametric and enables low-latency recognition, one-shot learning, and ac- tion detection in difficult unsegmented sequences. More- over, the framework is real-time, scalable, and outperforms more sophisticated approaches on challenging benchmarks like MSR-Action3D or MSR-DailyActivities3D.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Automatic Registration of RGB-D Scans via Salient Directions [pdf]
Bernhard Zeisl, Kevin Koser, Marc Pollefeys |
|
Abstract: We address the problem of wide-baseline registration of RGB-D data, such as photo-textured laser scans without any artificial targets or prediction on the relative motion. Our approach allows to fully automatically register scans taken in GPS-denied environments such as urban canyon, industrial facilities or even indoors. We build upon image features which are plenty, localized well and much more discriminative than geometry features; however, they suffer from viewpoint distortions and request for normalization.
We utilize the principle of salient directions present in the geometry and propose to extract (several) directions from the distribution of surface normals or other cues such as observable symmetries. Compared to previous work we pose no requirements on the scanned scene (like contain- ing large textured planes) and can handle arbitrary surface shapes. Rendering the whole scene from these repeatable directions using an orthographic camera generates textures which are identical up to 2D similarity transformations. This ambiguity is naturally handled by 2D features and al- lows to find stable correspondences among scans. For geo- metric pose estimation from tentative matches we propose a fast and robust 2 point sample consensus scheme integrat- ing an early rejection phase. We evaluate our approach on different challenging real world scenes.
|
Similar papers:
[rank all papers by similarity to this]
|
|
A Generic Deformation Model for Dense Non-rigid Surface Registration: A Higher-Order MRF-Based Approach [pdf]
Yun Zeng, Chaohui Wang, Xianfeng Gu, Dimitris Samaras, Nikos Paragios |
|
Abstract: We propose a novel approach for dense non-rigid 3D surface registration, which brings together Riemannian ge- ometry and graphical models. To this end, we first introduce a generic deformation model, called Canonical Distortion Coefficients (CDCs), by characterizing the deformation of every point on a surface using the distortions along its two principle directions. This model subsumes the deformation groups commonly used in surface registration such as isom- etry and conformality, and is able to handle more complex deformations. We also derive its discrete counterpart which can be computed very efficiently in a closed form. Based on these, we introduce a higher-order Markov Random Field (MRF) model which seamlessly integrates our deformation model and a geometry/texture similarity metric. Then we jointly establish the optimal correspondences for all the points via maximum a posteriori (MAP) inference. More- over, we develop a parallel optimization algorithm to effi- ciently perform the inference for the proposed higher-order MRF model. The resulting registration algorithm outper- forms state-of-the-art methods in both dense non-rigid 3D surface registration and tracking.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Geometric Registration Based on Distortion Estimation [pdf]
Wei Zeng, Mayank Goswami, Feng Luo, Xianfeng Gu |
|
Abstract: Surface registration plays a fundamental role in many applications in computer vision and aims at finding a one- to-one correspondence between surfaces. Conformal map- ping based surface registration methods conformally map 2D/3D surfaces onto 2D canonical domains and perform the matching on the 2D plane. This registration frame- work reduces dimensionality, and the result is intrinsic to Riemannian metric and invariant under isometric deforma- tion. However, conformal mapping will be affected by in- consistent boundaries and non-isometric deformations of surfaces. In this work, we quantify the effects of bound- ary variation and non-isometric deformation to conformal mappings, and give the theoretical upper bounds for the distortions of conformal mappings under these two factors. Besides giving the thorough theoretical proofs of the theo- rems, we verified them by concrete experiments using 3D human facial scans with dynamic expressions and varying boundaries. Furthermore, we used the distortion estimates for reducing search range in feature matching of surface registration applications. The experimental results are con- sistent with the theoretical predictions and also demonstrate the performance improvements in feature tracking.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Multi-stage Contextual Deep Learning for Pedestrian Detection [pdf]
Xingyu Zeng, Wanli Ouyang, Xiaogang Wang |
|
Abstract: Cascaded classifiers1 have been widely used in pedes- trian detection and achieved great success. These classi- fiers are trained sequentially without joint optimization. In this paper, we propose a new deep model that can jointly train multi-stage classifiers through several stages of back- propagation. It keeps the score map output by a classifier within a local region and uses it as contextual information to support the decision at the next stage. Through a spe- cific design of the training strategy, this deep architecture is able to simulate the cascaded classifiers by mining hard samples to train the network stage-by-stage. Each classi- fier handles samples at a different difficulty level. Unsu- pervised pre-training and specifically designed stage-wise supervised training are used to regularize the optimization problem. Both theoretical analysis and experimental re- sults show that the training strategy helps to avoid overfit- ting. Experimental results on three datasets (Caltech, ETH and TUD-Brussels) show that our approach outperforms the state-of-the-art approaches.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Deformable Part Descriptors for Fine-Grained Recognition and Attribute Prediction [pdf]
Ning Zhang, Ryan Farrell, Forrest Iandola, Trevor Darrell |
|
Abstract: Recognizing objects in fine-grained domains can be extremely challenging due to the subtle differences be- tween subcategories. Discriminative markings are often highly localized, leading traditional object recognition ap- proaches to struggle with the large pose variation often present in these domains. Pose-normalization seeks to align training exemplars, either piecewise by part or globally for the whole object, effectively factoring out differences in pose and in viewing angle. Prior approaches relied on computationally-expensive filter ensembles for part lo- calization and required extensive supervision. This pa- per proposes two pose-normalized descriptors based on computationally-efficient deformable part models. The first leverages the semantics inherent in strongly-supervised DPM parts. The second exploits weak semantic annota- tions to learn cross-component correspondences, comput- ing pose-normalized descriptors from the latent parts of a weakly-supervised DPM. These representations enable pooling across pose and viewpoint, in turn facilitating tasks such as fine-grained recognition and attribute prediction. Experiments conducted on the Caltech-UCSD Birds 200 dataset and Berkeley Human Attribute dataset demonstrate significant improvements over state-of-art algorithms.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Estimating the 3D Layout of Indoor Scenes and Its Clutter from Depth Sensors [pdf]
Jian Zhang, Chen Kan, Alexander G. Schwing, Raquel Urtasun |
|
Abstract: In this paper we propose an approach to jointly estimate the layout of rooms as well as the clutter present in the scene using RGB-D data. Towards this goal, we propose an effec- tive model that is able to exploit both depth and appearance features, which are complementary. Furthermore, our ap- proach is efficient as we exploit the inherent decomposition of additive potentials. We demonstrate the effectiveness of our approach on the challenging NYU v2 dataset and show that employing depth reduces the layout error by 6% and the clutter estimation by 13%.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Event Detection in Complex Scenes Using Interval Temporal Constraints [pdf]
Yifan Zhang, Qiang Ji, Hanqing Lu |
|
Abstract: In complex scenes with multiple atomic events happen- ing sequentially or in parallel, detecting each individual event separately may not always obtain robust and reli- able result. It is essential to detect them in a holistic way which incorporates the causality and temporal dependency among them to compensate the limitation of current com- puter vision techniques. In this paper, we propose an in- terval temporal constrained dynamic Bayesian network to extend Allens interval algebra network (IAN) [2] from a de- terministic static model to a probabilistic dynamic system, which can not only capture the complex interval temporal relationships, but also model the evolution dynamics and handle the uncertainty from the noisy visual observation. In the model, the topology of the IAN on each time slice and the interlinks between the time slices are discovered by an advanced structure learning method. The duration of the event and the unsynchronized time lags between two corre- lated event intervals are captured by a duration model, so that we can better determine the temporal boundary of the event. Empirical results on two real world datasets show the power of the proposed interval temporal constrained model.
|
Similar papers:
[rank all papers by similarity to this]
|
|
From Actemes to Action: A Strongly-Supervised Representation for Detailed Action Understanding [pdf]
Weiyu Zhang, Menglong Zhu, Konstantinos G. Derpanis |
|
Abstract: This paper presents a novel approach for analyzing
human actions in non-scripted, unconstrained video set-
tings based on volumetric, x-y-t, patch classifiers, termed
actemes. Unlike previous action-related work, the discov-
ery of patch classifiers is posed as a strongly-supervised
process. Specifically, keypoint labels (e.g., position) across
spacetime are used in a data-driven training process to dis-
cover patches that are highly clustered in the spacetime key-
point configuration space. To support this process, a new
human action dataset consisting of challenging consumer
videos is introduced, where notably the action label, the
2D position of a set of keypoints and their visibilities are
provided for each video frame. On a novel input video,
each acteme is used in a sliding volume scheme to yield
a set of sparse, non-overlapping detections. These detec-
tions provide the intermediate substrate for segmenting out the action. For action classification, the proposed represen- tation shows significant improvement over state-of-the-art low-level features, while providing spatiotemporal localiza- tion as additional output. This output sheds further light into detailed action understanding.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Learning CRFs for Image Parsing with Adaptive Subgradient Descent [pdf]
Honghui Zhang, Jingdong Wang, Ping Tan, Jinglu Wang, Long Quan |
|
Abstract: We propose an adaptive subgradient descent method to efficiently learn the parameters of CRF models for image parsing. To balance the learning efficiency and perfor- mance of the learned CRF models, the parameter learn- ing is iteratively carried out by solving a convex optimiza- tion problem in each iteration, which integrates a proximal term to preserve the previously learned information and the large margin preference to distinguish bad labeling and the ground truth labeling. A solution of subgradient descent up- dating form is derived for the convex optimization problem, with an adaptively determined updating step-size. Besides, to deal with partially labeled training data, we propose a new objective constraint modeling both the labeled and un- labeled parts in the partially labeled training data for the parameter learning of CRF models. The superior learning efficiency of the proposed method is verified by the experi- ment results on two public datasets. We also demonstrate the powerfulness of our method for handling partially la- beled training data.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Learning Graph Matching: Oriented to Category Modeling from Cluttered Scenes [pdf]
Quanshi Zhang, Xuan Song, Xiaowei Shao, Huijing Zhao, Ryosuke Shibasaki |
|
Abstract: Although graph matching is a fundamental problem in pattern recognition, and has drawn broad interest from many fields, the problem of learning graph matching has not received much attention. In this paper, we redefine the learning of graph matching as a model learning problem. In addition to conventional training of matching parameters, our approach modifies the graph structure and attributes to generate a graphical model. In this way, the model learning is oriented toward both matching and recognition perfor- mance, and can proceed in an unsupervised1 fashion. Ex- periments demonstrate that our approach outperforms con- ventional methods for learning graph matching.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Low-Rank Sparse Coding for Image Classification [pdf]
Tianzhu Zhang, Bernard Ghanem, Si Liu, Changsheng Xu, Narendra Ahuja |
|
Abstract: In this paper, we propose a low-rank sparse coding (LRSC) method that exploits local structure information a- mong features in an image for the purpose of image-level classification. LRSC represents densely sampled SIFT de- scriptors, in a spatial neighborhood, collectively as low- rank, sparse linear combinations of codewords. As such, it casts the feature coding problem as a low-rank matrix learn- ing problem, which is different from previous methods that encode features independently. This LRSC has a number of attractive properties. (1) It encourages sparsity in feature codes, locality in codebook construction, and low-rankness for spatial consistency. (2) LRSC encodes local features jointly by considering their low-rank structure information, and is computationally attractive. We evaluate the LRSC by comparing its performance on a set of challenging bench- marks with that of 7 popular coding and other state-of-the- art methods. Our experiments show that by representing lo- cal features jointly, LRSC not only outperforms the state-of- the-art in classification accuracy but also improves the time complexity of methods that use a similar sparse linear repre- sentation model for feature coding [36].
|
Similar papers:
[rank all papers by similarity to this]
|
|
Random Faces Guided Sparse Many-to-One Encoder for Pose-Invariant Face Recognition [pdf]
Yizhe Zhang, Ming Shao, Edward K. Wong, Yun Fu |
|
Abstract: One of the most challenging task in face recognition is to identify people with varied poses. Namely, the test faces have significantly different poses compared with the reg- istered faces. In this paper, we propose a high-level fea- ture learning scheme to extract pose-invariant identity fea- ture for face recognition. First, we build a single-hidden- layer neural network with sparse constraint, to extract pose- invariant feature in a supervised fashion. Second, we fur- ther enhance the discriminative capability of the proposed feature by using multiple random faces as the target values for multiple encoders. By enforcing the target values to be unique for input faces over different poses, the learned high- level feature that is represented by the neurons in the hidden layer is pose free and only relevant to the identity informa- tion. Finally, we conduct face identification on CMU Multi- PIE, and verification on Labeled Faces in the Wild (LFW) databases, where identification rank-1 accuracy and face verification accuracy with ROC curve are reported. These experiments demonstrate that our model is superior to oth- er state-of-the-art approaches on handling pose variations.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Robust Subspace Clustering via Half-Quadratic Minimization [pdf]
Yingya Zhang, Zhenan Sun, Ran He, Tieniu Tan |
|
Abstract: Subspace clustering has important and wide applica- tions in computer vision and pattern recognition. It is a challenging task to learn low-dimensional subspace struc- tures due to the possible errors (e.g., noise and corruptions) existing in high-dimensional data. Recent subspace clus- tering methods usually assume a sparse representation of corrupted errors and correct the errors iteratively. How- ever large corruptions in real-world applications can not be well addressed by these methods. A novel optimization model for robust subspace clustering is proposed in this pa- per. The objective function of our model mainly includes two parts. The first part aims to achieve a sparse represen- tation of each high-dimensional data point with other data points. The second part aims to maximize the correntropy between a given data point and its low-dimensional repre- sentation with other points. Correntropy is a robust mea- sure so that the influence of large corruptions on subspace clustering can be greatly suppressed. An extension of our method with explicit introduction of representation error terms into the model is also proposed. Half-quadratic mini- mization is provided as an efficient solution to the proposed robust subspace clustering formulations. Experimental re- sults on Hopkins 155 dataset and Extended Yale Database B demonstrate that our method outperforms state-of-the-art subspace clustering methods.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: Many tensor based algorithms have been proposed for the study of high dimensional data in a large variety of com- puter vision and machine learning applications. However, most of the existing tensor analysis approaches are based on Frobenius norm, which makes them sensitive to outliers, because they minimize the sum of squared errors and en- large the influence of both outliers and large feature noises. In this paper, we propose a robust Tucker tensor decom- position model (RTD) to suppress the influence of outliers, which uses L1-norm loss function. Yet, the optimization on L1-norm based tensor analysis is much harder than stan- dard tensor decomposition. In this paper, we propose a sim- ple and efficient algorithm to solve our RTD model. More- over, tensor factorization-based image storage needs much less space than PCA based methods. We carry out extensive experiments to evaluate the proposed algorithm, and verify the robustness against image occlusions. Both numerical and visual results show that our RTD model is consistently better against the existence of outliers than previous tensor and PCA methods.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: A novel Boolean Map based Saliency (BMS) model is proposed. An image is characterized by a set of binary images, which are generated by randomly thresholding the images color channels. Based on a Gestalt principle of figure-ground segregation, BMS computes saliency maps by analyzing the topological structure of Boolean maps. BMS is simple to implement and efficient to run. Despite its simplicity, BMS consistently achieves state-of-the-art performance compared with ten leading methods on five eye tracking datasets. Furthermore, BMS is also shown to be advantageous in salient object detection.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Semantic-Aware Co-indexing for Image Retrieval [pdf]
Shiliang Zhang, Ming Yang, Xiaoyu Wang, Yuanqing Lin, Qi Tian |
|
Abstract: Inverted indexes in image retrieval not only allow fast access to database images but also summarize all knowl- edge about the database, so that their discriminative ca- pacity largely determines the retrieval performance. In this paper, for vocabulary tree based image retrieval, we propose a semantic-aware co-indexing algorithm to jointly embed two strong cues into the inverted indexes: 1) local invariant features that are robust to delineate low-level im- age contents, and 2) semantic attributes from large-scale object recognition that may reveal image semantic mean- ings. For an initial set of inverted indexes of local features, we utilize 1000 semantic attributes to filter out isolated im- ages and insert semantically similar images to the initial set. Encoding these two distinct cues together effectively enhances the discriminative capability of inverted indexes. Such co-indexing operations are totally off-line and intro- duce small computation overhead to online query cause only local features but no semantic attributes are used for query. Experiments and comparisons with recent retrieval methods on 3 datasets, i.e., UKbench, Holidays, Oxford5K, and 1.3 million images from Flickr as distractors, manifest the competitive performance of our method 1.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Toward Guaranteed Illumination Models for Non-convex Objects [pdf]
Yuqian Zhang, Cun Mu, Han-Wen Kuo, John Wright |
|
Abstract: Illumination variation remains a central challenge in ob- ject detection and recognition. Existing analyses of illumi- nation variation typically pertain to convex, Lambertian ob- jects, and guarantee quality of approximation in an average case sense. We show that it is possible to build models for the set of images across illumination variation with worst- case performance guarantees, for nonconvex Lambertian objects. Namely, a natural verification test based on the dis- tance to the model guarantees to accept any image which can be sufficiently well-approximated by an image of the object under some admissible lighting condition, and guar- antees to reject any image that does not have a sufficiently good approximation. These models are generated by sam- pling illumination directions with sufficient density, which follows from a new perturbation bound for directional illu- minated images in the Lambertian model. As the number of such images required for guaranteed verification may be large, we introduce a new formulation for cone preserving dimensionality reduction, which leverages tools from sparse and low-rank decomposition to reduce the complexity, while controlling the approximation error with respect to the orig- inal model.1
|
Similar papers:
[rank all papers by similarity to this]
|
|
Understanding High-Level Semantics by Modeling Traffic Patterns [pdf]
Hongyi Zhang, Andreas Geiger, Raquel Urtasun |
|
Abstract: In this paper, we are interested in understanding the se- mantics of outdoor scenes in the context of autonomous driving. Towards this goal, we propose a generative model of 3D urban scenes which is able to reason not only about the geometry and objects present in the scene, but also about the high-level semantics in the form of traffic pat- terns. We found that a small number of patterns is suffi- cient to model the vast majority of traffic scenes and show how these patterns can be learned. As evidenced by our ex- periments, this high-level reasoning significantly improves the overall scene estimation as well as the vehicle-to-lane association when compared to state-of-the-art approaches [10].
|
Similar papers:
[rank all papers by similarity to this]
|
|
Cascaded Shape Space Pruning for Robust Facial Landmark Detection [pdf]
Xiaowei Zhao, Shiguang Shan, Xiujuan Chai, Xilin Chen |
|
Abstract: In this paper, we propose a novel cascaded face shape space pruning algorithm for robust facial landmark detec- tion. Through progressively excluding the incorrect candi- date shapes, our algorithm can accurately and efficiently achieve the globally optimal shape configuration. Specif- ically, individual landmark detectors are firstly applied to eliminate wrong candidates for each landmark. Then, the candidate shape space is further pruned by jointly remov- ing incorrect shape configurations. To achieve this purpose, a discriminative structure classifier is designed to assess the candidate shape configurations. Based on the learned discriminative structure classifier, an efficient shape space pruning strategy is proposed to quickly reject most incorrect candidate shapes while preserve the true shape. The pro- posed algorithm is carefully evaluated on a large set of real world face images. In addition, comparison results on the publicly available BioID and LFW face databases demon- strate that our algorithm outperforms some state-of-the-art algorithms.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: Human salience is distinctive and reliable information in matching pedestrians across disjoint camera views. In this paper, we exploit the pairwise salience distribution re- lationship between pedestrian images, and solve the person re-identification problem by proposing a salience matching strategy. To handle the misalignment problem in pedes- trian images, patch matching is adopted and patch salience is estimated. Matching patches with inconsistent salience brings penalty. Images of the same person are recognized by minimizing the salience matching cost. Furthermore, our salience matching is tightly integrated with patch match- ing in a unified structural RankSVM learning framework. The effectiveness of our approach is validated on the VIPeR dataset and the CUHK Campus dataset. It outperforms the state-of-the-art methods on both datasets.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: We handle a special type of motion blur considering that cameras move primarily forward or backward. Solving this type of blur is of unique practical importance since nearly all car, traffic and bike-mounted cameras follow out-of- plane translational motion. We start with the study of geo- metric models and analyze the difficulty of existing methods to deal with them. We also propose a solution accounting for depth variation. Homographies associated with differ- ent 3D planes are considered and solved for in an optimiza- tion framework. Our method is verified on several natural image examples that cannot be satisfyingly dealt with by previous methods.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Learning View-Invariant Sparse Representations for Cross-View Action Recognition [pdf]
Jingjing Zheng, Zhuolin Jiang |
|
Abstract: We present an approach to jointly learn a set of view- specific dictionaries and a common dictionary for cross- view action recognition. The set of view-specific dictionar- ies is learned for specific views while the common dictio- nary is shared across different views. Our approach rep- resents videos in each view using both the corresponding view-specific dictionary and the common dictionary. More importantly, it encourages the set of videos taken from dif- ferent views of the same action to have similar sparse rep- resentations. In this way, we can align view-specific fea- tures in the sparse feature spaces spanned by the view- specific dictionary set and transfer the view-shared features in the sparse feature space spanned by the common dic- tionary. Meanwhile, the incoherence between the common dictionary and the view-specific dictionary set enables us to exploit the discrimination information encoded in view- specific features and view-shared features separately. In addition, the learned common dictionary not only has the capability to represent actions from unseen views, but also makes our approach effective in a semi-supervised setting where no correspondence videos exist and only a few labels exist in the target view. Extensive experiments using the multi-view IXMAS dataset demonstrate that our approach outperforms many recent approaches for cross-view action recognition.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Revisiting the PnP Problem: A Fast, General and Optimal Solution [pdf]
Yinqiang Zheng, Yubin Kuang, Shigeki Sugimoto, Kalle Astrom, Masatoshi Okutomi |
|
Abstract: In this paper, we revisit the classical perspective-n-point (PnP) problem, and propose the first non-iterative O(n) so- lution that is fast, generally applicable and globally opti- mal. Our basic idea is to formulate the PnP problem into a functional minimization problem and retrieve all its sta- tionary points by using the Gro bner basis technique. The novelty lies in a non-unit quaternion representation to pa- rameterize the rotation and a simple but elegant formula- tion of the PnP problem into an unconstrained optimization problem. Interestingly, the polynomial system arising from its first-order optimality condition assumes two-fold sym- metry, a nice property that can be utilized to improve speed and numerical stability of a Gro bner basis solver. Experi- ment results have demonstrated that, in terms of accuracy, our proposed solution is definitely better than the state-of- the-art O(n) methods, and even comparable with the repro- jection error minimization method.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: We present an approach to reconstruction of detailed scene geometry from range video. Range data produced by commodity handheld cameras suffers from high-frequency errors and low-frequency distortion. Our approach deals with both sources of error by reconstructing locally smooth scene fragments and letting these fragments deform in or- der to align to each other. We develop a volumetric regis- tration formulation that leverages the smoothness of the de- formation to make optimization practical for large scenes. Experimental results demonstrate that our approach sub- stantially increases the fidelity of complex scene geometry reconstructed with commodity handheld cameras.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Enhanced Continuous Tabu Search for Parameter Estimation in Multiview Geometry [pdf]
Guoqing Zhou, Qing Wang |
|
Abstract: Optimization using the L norm has been becoming an effective way to solve parameter estimation problems in multiview geometry. But the computational cost increases rapidly with the size of measurement data. Although some strategies have been presented to improve the efficiency of L optimization, it is still an open issue. In the paper, we propose a novel approach under the framework of enhanced continuous tabu search (ECTS) for generic parameter es- timation in multiview geometry. ECTS is an optimization method in the domain of artificial intelligence, which has an interesting ability of covering a wide solution space by promoting the search far away from current solution and consecutively decreasing the possibility of trapping in the local minima. Taking the triangulation as an example, we propose the corresponding ways in the key steps of ECTS, diversification and intensification. We also present theoret- ical proof to guarantee the global convergence of search with probability one. Experimental results have validated that the ECTS based approach can obtain global optimum efficiently, especially for large scale dimension of param- eter. Potentially, the novel ECTS based algorithm can be applied in many applications of multiview geometry.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Exemplar-Based Graph Matching for Robust Facial Landmark Localization [pdf]
Feng Zhou, Jonathan Brandt, Zhe Lin |
|
Abstract: Localizing facial landmarks is a fundamental step in fa- cial image analysis. However, the problem is still challeng- ing due to the large variability in pose and appearance, and the existence of occlusions in real-world face images. In this paper, we present exemplar-based graph matching (EGM), a robust framework for facial landmark localization. Com- pared to conventional algorithms, EGM has three advan- tages: (1) an affine-invariant shape constraint is learned online from similar exemplars to better adapt to the test face; (2) the optimal landmark configuration can be di- rectly obtained by solving a graph matching problem with the learned shape constraint; (3) the graph matching prob- lem can be optimized efficiently by linear programming. To our best knowledge, this is the first attempt to apply a graph matching technique for facial landmark localization. Ex- periments on several challenging datasets demonstrate the advantages of EGM over state-of-the-art methods.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: Sharing knowledge for multiple related machine learn- ing tasks is an effective strategy to improve the generaliza- tion performance. In this paper, we investigate knowledge sharing across categories for action recognition in videos. The motivation is that many action categories are related, where common motion pattern are shared among them (e.g. diving and high jump share the jump motion). We propose a new multi-task learning method to learn latent tasks shared across categories, and reconstruct a classifier for each cat- egory from these latent tasks. Compared to previous meth- ods, our approach has two advantages: (1) The learned la- tent tasks correspond to basic motion patterns instead of full actions, thus enhancing discrimination power of the classi- fiers. (2) Categories are selected to share information with a sparsity regularizer, avoiding falsely forcing all categories to share knowledge. Experimental results on multiple public data sets show that the proposed approach can effectively transfer knowledge between different action categories to improve the performance of conventional single task learn- ing methods.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Abstract: With the improved accessibility to an exploding amoun- t of video data and growing demands in a wide range of video analysis applications, video-based action recogni- tion/classification becomes an increasingly important task in computer vision. In this paper, we propose a two-layer structure for action recognition to automatically exploit a mid-level acton representation. The weakly-supervised actons are learned via a new max-margin multi-channel multiple instance learning framework, which can capture multiple mid-level action concepts simultaneously. The learned actons (with no requirement for detailed manual annotations) observe the properties of being compact, infor- mative, discriminative, and easy to scale. The experimental results demonstrate the effectiveness of applying the learned actons in our two-layer structure, and show the state-of- the-art recognition performance on two challenging action datasets, i.e., Youtube and HMDB51.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Deep Learning Identity-Preserving Face Space [pdf]
Zhenyao Zhu, Ping Luo, Xiaogang Wang, Xiaoou Tang |
|
Abstract: Face recognition with large pose and illumination varia- tions is a challenging problem in computer vision. This pa- per addresses this challenge by proposing a new learning- based face representation: the face identity-preserving (FIP) features. Unlike conventional face descriptors, the FIP features can significantly reduce intra-identity variances, while maintaining discriminativeness between identities. Moreover, the FIP features extracted from an image under any pose and illumination can be used to reconstruct its face image in the canonical view. This property makes it possible to improve the performance of traditional descriptors, such as LBP [2] and Gabor [31], which can be extracted from our reconstructed images in the canonical view to eliminate variations. In order to learn the FIP features, we carefully design a deep network that combines the feature extraction layers and the recon- struction layer. The former encodes a face image into the FIP features, while the latter transforms them to an image in the canonical view. Extensive experiments on the large MultiPIE face database [7] demonstrate that it significantly outperforms the state-of-the-art face recognition methods.
|
Similar papers:
[rank all papers by similarity to this]
|
|
From Point to Set: Extend the Learning of Distance Metrics [pdf]
Pengfei Zhu, Lei Zhang, Wangmeng Zuo, David Zhang |
|
Abstract: Most of the current metric learning methods are pro- posed for point-to-point distance (PPD) based classifica- tion. In many computer vision tasks, however, we need to measure the point-to-set distance (PSD) and even set-to-set distance (SSD) for classification. In this paper, we extend the PPD based Mahalanobis distance metric learning to PSD and SSD based ones, namely point-to-set distance met- ric learning (PSDML) and set-to-set distance metric learn- ing (SSDML), and solve them under a unified optimization framework. First, we generate positive and negative sam- ple pairs by computing the PSD and SSD between train- ing samples. Then, we characterize each sample pair by its covariance matrix, and propose a covariance kernel based discriminative function. Finally, we tackle the PSDML and SSDML problems by using standard support vector machine solvers, making the metric learning very efficient for multi- class visual classification tasks. Experiments on gender classification, digit recognition, object categorization and face recognition show that the proposed metric learning methods can effectively enhance the performance of PSD and SSD based classification.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Query-Adaptive Asymmetrical Dissimilarities for Visual Object Retrieval [pdf]
Cai-Zhi Zhu, Herve Jegou, Shin'Ichi Satoh |
|
Abstract: Visual object retrieval aims at retrieving, from a col- lection of images, all those in which a given query object appears. It is inherently asymmetric: the query object is mostly included in the database image, while the converse is not necessarily true. However, existing approaches mostly compare the images with symmetrical measures, without considering the different roles of query and database.
This paper first measure the extent of asymmetry on large-scale public datasets reflecting this task. Considering the standard bag-of-words representation, we then propose new asymmetrical dissimilarities accounting for the differ- ent inlier ratios associated with query and database images. These asymmetrical measures depend on the query, yet they are compatible with an inverted file structure, without no- ticeably impacting search efficiency. Our experiments show the benefit of our approach, and show that the visual object retrieval task is better treated asymmetrically, in the spirit of state-of-the-art text retrieval.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Video Synopsis by Heterogeneous Multi-source Correlation [pdf]
Xiatian Zhu, Chen Change Loy, Shaogang Gong |
|
Abstract: Generating coherent synopsis for surveillance video stream remains a formidable challenge due to the ambi- guity and uncertainty inherent to visual observations. In contrast to existing video synopsis approaches that rely on visual cues alone, we propose a novel multi-source synopsis framework capable of correlating visual data and indepen- dent non-visual auxiliary information to better describe and summarise subtle physical events in complex scenes. Specif- ically, our unsupervised framework is capable of seamlessly uncovering latent correlations among heterogeneous types of data sources, despite the non-trivial heteroscedasticity and dimensionality discrepancy problems. Additionally, the proposed model is robust to partial or missing non-visual information. We demonstrate the effectiveness of our frame- work on two crowded public surveillance datasets.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Learning the Visual Interpretation of Sentences [pdf]
C. Lawrence Zitnick, Devi Parikh, Lucy Vanderwende |
|
Abstract: Sentences that describe visual scenes contain a wide va- riety of information pertaining to the presence of objects, their attributes and their spatial relations. In this paper we learn the visual features that correspond to semantic phrases derived from sentences. Specifically, we extract predicate tuples that contain two nouns and a relation. The relation may take several forms, such as a verb, preposition, adjective or their combination. We model a scene using a Conditional Random Field (CRF) formulation where each node corresponds to an object, and the edges to their rela- tions. We determine the potentials of the CRF using the tu- ples extracted from the sentences. We generate novel scenes depicting the sentences visual meaning by sampling from the CRF. The CRF is also used to score a set of scenes for a text-based image retrieval task. Our results show we can generate (retrieve) scenes that convey the desired seman- tic meaning, even when scenes (queries) are described by multiple sentences. Significant improvement is found over several baseline approaches.
|
Similar papers:
[rank all papers by similarity to this]
|
|
Estimating Human Pose with Flowing Puppets [pdf]
Silvia Zuffi, Javier Romero, Cordelia Schmid, Michael J. Black |
|
Abstract: We address the problem of upper-body human pose esti- mation in uncontrolled monocular video sequences, without manual initialization. Most current methods focus on iso- lated video frames and often fail to correctly localize arms and hands. Inferring pose over a video sequence is advan- tageous because poses of people in adjacent frames exhibit properties of smooth variation due to the nature of human and camera motion. To exploit this, previous methods have used prior knowledge about distinctive actions or generic temporal priors combined with static image likelihoods to track people in motion. Here we take a different approach based on a simple observation: Information about how a person moves from frame to frame is present in the opti- cal flow field. We develop an approach for tracking articu- lated motions that links articulated shape models of peo- ple in adjacent frames through the dense optical flow. Key to this approach is a 2D shape model of the body that we use to compute how the body moves over time. The result- ing flowing puppets provide a way of integrating image evidence across frames to improve pose inference. We apply our method on a challenging dataset of TV video sequences and show state-of-the-art performance.
|
Similar papers:
[rank all papers by similarity to this]
|
|
A Generalized Iterated Shrinkage Algorithm for Non-convex Sparse Coding [pdf]
Wangmeng Zuo, Deyu Meng, Lei Zhang, Xiangchu Feng, David Zhang |
|
Abstract: In many sparse coding based image restoration and im- age classification problems, using non-convex lp-norm min- imization (0 p < 1) can often obtain better results than the convex l1-norm minimization. A number of algorithms, e.g., iteratively reweighted least squares (IRLS), iterative- ly thresholding method (ITM-l p ), and look-up table (LUT), have been proposed for non-convex lp-norm sparse coding, while some analytic solutions have been suggested for some specific values of p. In this paper, by extending the popular soft-thresholding operator, we propose a generalized iter- ated shrinkage algorithm (GISA) for lp-norm non-convex sparse coding. Unlike the analytic solutions, the proposed GISA algorithm is easy to implement, and can be adopted for solving non-convex sparse coding problems with arbi- trary p values. Compared with LUT, GISA is more gen- eral and does not need to compute and store the look-up tables. Compared with IRLS and ITM-lp, GISA is theoret- ically more solid and can achieve more accurate solutions. Experiments on image restoration and sparse coding based face recognition are conducted to validate the performance of GISA.
|
Similar papers:
[rank all papers by similarity to this]
|