mtybilly/Instruct2DS huggingface dataset:
Action Recognition with Improved Trajectories
Authors: Heng Wang, Cordelia Schmid
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

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 paper improves their performance by taking into account camera motion to correct them. To estimate camera motion, we match feature points between frames using SURF descriptors and dense optical flow, which are shown to be complementary. These matches are, then, used to robustly estimate a homography with RANSAC. Human motion is in general different from camera motion and generates inconsistent matches. To improve the estimation, a human detector is employed to remove these matches. Given the estimated 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. Experimental results on four challenging action datasets (i.e., Hollywood2, HMDB51, Olympic Sports and UCF50) significantly outperform the current state of the art.
A Generic Deformation Model for Dense Non-rigid Surface Registration: A Higher-Order MRF-Based Approach
Authors: Yun Zeng, Chaohui Wang, Xianfeng Gu, Dimitris Samaras, Nikos Paragios
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: We propose a novel approach for dense non-rigid 3D surface registration, which brings together Riemannian geometry 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 isometry 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. Moreover, we develop a parallel optimization algorithm to efficiently perform the inference for the proposed higher-order MRF model. The resulting registration algorithm outperforms state-of-the-art methods in both dense non-rigid 3D surface registration and tracking.
Joint Inverted Indexing
Authors: Yan Xia, Kaiming He, Fang Wen, Jian Sun
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

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 recall and speed. Instead of computing the multiple quantizers independently, 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 experiments this method shows significant improvement over various existing methods that use multiple independent quantizers. 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.
From Point to Set: Extend the Learning of Distance Metrics
Authors: Pengfei Zhu, Lei Zhang, Wangmeng Zuo, David Zhang
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Most of the current metric learning methods are proposed for point-to-point distance (PPD) based classification. 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 metric learning (PSDML) and set-to-set distance metric learning (SSDML), and solve them under a unified optimization framework. First, we generate positive and negative sample pairs by computing the PSD and SSD between training 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 multiclass 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.
Learning View-Invariant Sparse Representations for Cross-View Action Recognition
Authors: Jingjing Zheng, Zhuolin Jiang
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: We present an approach to jointly learn a set of viewspecific dictionaries and a common dictionary for crossview action recognition. The set of view-specific dictionaries is learned for specific views while the common dictionary is shared across different views. Our approach represents 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 different views of the same action to have similar sparse representations. In this way, we can align view-specific features in the sparse feature spaces spanned by the viewspecific dictionary set and transfer the view-shared features in the sparse feature space spanned by the common dictionary. Meanwhile, the incoherence between the common dictionary and the view-specific dictionary set enables us to exploit the discrimination information encoded in viewspecific 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.
Line Assisted Light Field Triangulation and Stereo Matching
Authors: Zhan Yu, Xinqing Guo, Haibing Lin, Andrew Lumsdaine, Jingyi Yu
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

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 matching. 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 superresolution. We show that the light field space is largely bilinear due to 3D line segments in the scene, and direct triangulation 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 graphcut (LAGC) algorithm that effectively encodes 3D line constraints 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.
Towards Motion Aware Light Field Video for Dynamic Scenes
Authors: Salil Tambe, Ashok Veeraraghavan, Amit Agrawal
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

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, capturing high spatial resolution LF video for dynamic scenes remains 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 redundancy in space, time and angle. Our reconstruction is motion-aware and offers a continuum of resolution tradeoff with increasing motion in the scene. The key idea is (a) to design efficient multiplexing matrices that allow resolution tradeoffs, (b) use dictionary learning and sparse representations for robust reconstruction, and (c) perform local motion-aware adaptive reconstruction. We perform extensive analysis and characterize the performance 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 resolution digital refocusing for dynamic moving objects.
Abnormal Event Detection at 150 FPS in MATLAB
Authors: Cewu Lu, Jianping Shi, Jiaya Jia
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

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 framework. 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 140~150 frames per second on average when computing on an ordinary desktop PC using MATLAB.
Elastic Fragments for Dense Scene Reconstruction
Authors: Qian-Yi Zhou, Stephen Miller, Vladlen Koltun
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

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 order to align to each other. We develop a volumetric registration formulation that leverages the smoothness of the deformation to make optimization practical for large scenes. Experimental results demonstrate that our approach substantially increases the fidelity of complex scene geometry reconstructed with commodity handheld cameras.
Shape Anchors for Data-Driven Multi-view Reconstruction
Authors: Andrew Owens, Jianxiong Xiao, Antonio Torralba, William Freeman
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

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 recoggnioon.WWWeccalltteeseepatchhesshhapeaanchorrs,anndwwe 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 relatively 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.
Piecewise Rigid Scene Flow
Authors: Christoph Vogel, Konrad Schindler, Stefan Roth
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

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 overcome 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 pixelto-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 regularizers. 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 assignment 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.
Spoken Attributes: Mixing Binary and Relative Attributes to Say the Right Thing
Authors: Amir Sadovnik, Andrew Gallagher, Devi Parikh, Tsuhan Chen
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

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 verification, 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 comparing two exemplars. However, prior work fails to model the actual way in which humans use these attributes in descriptive 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 attribute 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.
Coupled Dictionary and Feature Space Learning with Applications to Cross-Domain Image Synthesis and Recognition
Authors: De-An Huang, Yu-Chiang Frank Wang
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Cross-domain image synthesis and recognition are typically considered as two distinct tasks in the areas of computer vision and pattern recognition. Therefore, it is not clear whether approaches addressing one task can be easily 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 derived 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 variety of synthesis and recognition tasks such as single image super-resolution, cross-view action recognition, and sketchto-photo face recognition would verify the effectiveness of our proposed learning model.
Hierarchical Part Matching for Fine-Grained Visual Categorization
Authors: Lingxi Xie, Qi Tian, Richang Hong, Shuicheng Yan, Bo Zhang
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: As a special topic in computer vision, fine-grained visual categorization (FGVC) has been attracting growing attention these years. Different with traditional image classification tasks in which objects have large inter-class variation, the visual concepts in the fine-grained datasets, such as hundreds of bird species, often have very similar semantics. 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 algorithm 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 finegrained classification tasks. We extend the Bag-of-Features (BoF) model by introducing several novel modules to integrate into image representation, including foreground inference and segmentation, Hierarchical Structure Learning (HSL), and Geometric Phrase Pooling (GPP). We verify in experiments that our algorithm achieves the state-ofthe-art classification accuracy in the Caltech-UCSD-Birds200-2011 dataset by making full use of the ground-truth part annotations.
Locally Affine Sparse-to-Dense Matching for Motion and Occlusion Estimation
Authors: Marius Leordeanu, Andrei Zanfir, Cristian Sminchisescu
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Estimating a dense correspondence field between successive video frames, under large displacement, is important in many visual learning and recognition tasks. We propose a novel sparse-to-dense matching method for motion field estimation and occlusion detection. As an alternative to the current coarse-to-fine approaches from the optical 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 interpolation procedure that offers a natural transition between the sparse and the dense correspondence fields. We experimentally demonstrate that our appearance features and our complex geometric constraints permit the correct motion estimation 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.
SUN3D: A Database of Big Spaces Reconstructed Using SfM and Object Labels
Authors: Jianxiong Xiao, Andrew Owens, Antonio Torralba
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

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 difficult 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 construction task much easier. First, we introduce an intuitive labeling tool that uses a partial reconstruction to propagate 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 incorporates object-to-object correspondences. This algorithm works by constraining points for the same object from different frames to lie inside a fixed-size bounding box, parameterized by its rotation and translation. The SUN3D database, the source code for the generalized bundle adjustment, and the web-based 3D annotation tool are all available at http://sun3d.cs.princeton.edu.
A Deep Sum-Product Architecture for Robust Facial Attributes Analysis
Authors: Ping Luo, Xiaogang Wang, Xiaoou Tang
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

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 estimation, 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. Second, 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 region's localization and classification. Experimental results on a large data set with 22, 400 images show the effectiveness of the proposed approach.
Point-Based 3D Reconstruction of Thin Objects
Authors: Benjamin Ummenhofer, Thomas Brox
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

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 reconstruction with respect to shape representation and fusion of depth information. In this paper we present a dense pointbased 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 optimization 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.
Structured Learning of Sum-of-Submodular Higher Order Energy Functions
Authors: Alexander Fix, Thorsten Joachims, Sung Min Park, Ramin Zabih
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Submodular functions can be exactly minimized in polynomial time, and the special case that graph cuts solve with max flow [19] has had significant impact in computer vision [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 function, 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 training problem in terms of quadratic programming; as a result 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 segmentation technique [28], which uses hand-tuned parameters instead of machine learning. On a standard dataset [12] our method learns higher order priors with hundreds of parameter values, and produces significantly better segmentations. While our focus is on binary labeling problems, we show that our techniques can be naturally generalized to handle more than two labels.
Affine-Constrained Group Sparse Coding and Its Application to Image-Based Classifications
Authors: Yu-Tseh Chi, Mohsen Ali, Muhammad Rushdi, Jeffrey Ho
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

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), extends the current SRC framework to classification problems with multiple input samples. Geometrically, the affineconstrained 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 efficiently 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 experiments that involve illumination variation of faces and textures, and face recognition under occlusions. Preliminary experiments have demonstrated the effectiveness of the proposed approach, and in particular, the results from the recognition/occlusion experiment are surprisingly accurate and robust.
Latent Space Sparse Subspace Clustering
Authors: Vishal M. Patel, Hien Van Nguyen, Rene Vidal
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: We propose a novel algorithm called Latent Space Sparse Subspace Clustering for simultaneous dimensionality reduction and clustering of data lying in a union of subspaces. 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 assigned by applying spectral clustering to a similarity matrix built from these sparse coefficients. An efficient optimization method is proposed and its non-linear extensions based on the kernel methods are presented. One of the main advantages 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-theart subspace clustering methods.
To Aggregate or Not to aggregate: Selective Match Kernels for Image Search
Authors: Giorgos Tolias, Yannis Avrithis, Herve Jegou
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: This paper considers a family of metrics to compare images based on their local descriptors. It encompasses the VLAD descriptor and matching techniques such as Hamming Embedding. Making the bridge between these approaches 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 representation underpinning this kernel is approximated, providing a large scale image search both precise and scalable, as shown by our experiments on several benchmarks.
Person Re-identification by Salience Matching
Authors: Rui Zhao, Wanli Ouyang, Xiaogang Wang
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Human salience is distinctive and reliable information in matching pedestrians across disjoint camera views. In this paper, we exploit the pairwise salience distribution relationship between pedestrian images, and solve the person re-identification problem by proposing a salience matching strategy. To handle the misalignment problem in pedestrian 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 matching 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.
Pose Estimation with Unknown Focal Length Using Points, Directions and Lines
Authors: Yubin Kuang, Kalle Astrom
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: In this paper, we study the geometry problems of estimating camera pose with unknown focal length using combination 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 polynomial systems where the constraints for different primitives are handled in a unified way. We develop efficient polynomial 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 stable. For real images, we show that our solvers can be used in RANSAC loops to provide good initial solutions.
Action Recognition and Localization by Hierarchical Space-Time Segments
Authors: Shugao Ma, Jianming Zhang, Nazli Ikizler-Cinbis, Stan Sclaroff
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

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 representation from video, which extracts both static and non-static relevant space-time segments, and also preserves their hierarchical and temporal relationships. Using simple linear SVM on the resultant bag of hierarchical space-time segments 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 produce good action localization results.
A General Dense Image Matching Framework Combining Direct and Feature-Based Costs
Authors: Jim Braux-Zin, Romain Dupont, Adrien Bartoli
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

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 compute 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 combines 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 contain 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 order 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 surface registration). Our framework has a modular design that customizes to specific application needs.
Fast Neighborhood Graph Search Using Cartesian Concatenation
Authors: Jing Wang, Jingdong Wang, Gang Zeng, Rui Gan, Shipeng Li, Baining Guo
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: In this paper, we propose a new data structure for approximate nearest neighbor search. This structure augments the neighborhood graph with a bridge graph. We propose 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. Experimental results on searching over large scale datasets (SIFT, GIST and HOG) show that our approach outperforms stateof-the-art ANN search algorithms in terms of efficiency and accuracy. The combination of our approach with the IVFADC system [18] also shows superior performance over the BIGANN dataset of 1 billion SIFT features compared with the best previously published result.
Uncertainty-Driven Efficiently-Sampled Sparse Graphical Models for Concurrent Tumor Segmentation and Atlas Registration
Authors: Sarah Parisot, William Wells III, Stephane Chemouny, Hugues Duffau, Nikos Paragios
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Graph-based methods have become popular in recent years and have successfully addressed tasks like segmentation and deformable registration. Their main strength is optimality of the obtained solution while their main limitation is the lack of precision due to the grid-like representations and the discrete nature of the quantized search space. In this paper we introduce a novel approach for combined segmentation/registration of brain tumors that adapts graph and sampling resolution according to the image content. To this end we estimate the segmentation and registration marginals towards adaptive graph resolution and intelligent definition of the search space. This information is considered in a hierarchical framework where uncertainties are propagated in a natural manner. State of the art results in the joint segmentation/registration of brain images with low-grade gliomas demonstrate the potential of our approach.
Learning Near-Optimal Cost-Sensitive Decision Policy for Object Detection
Authors: Tianfu Wu, Song-Chun Zhu
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Many object detectors, such as AdaBoost, SVM and deformable part-based models (DPM), compute additive scoring functions at a large number of windows scanned over image pyramid, thus computational efficiency is an important consideration beside accuracy performance. In this paper, we present a framework of learning cost-sensitive decision policy which is a sequence of two-sided thresholds 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.
Coherent Object Detection with 3D Geometric Context from a Single Image
Authors: Jiyan Pan, Takeo Kanade
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Objects in a real world image cannot have arbitrary appearance, sizes and locations due to geometric constraints in 3D space. Such a 3D geometric context plays an important role in resolving visual ambiguities and achieving coherent object detection. In this paper, we develop a RANSAC-CRF framework to detect objects that are geometrically coherent in the 3D world. Different from existing methods, we propose a novel generalized RANSAC algorithm to generate global 3D geometry hypotheses from local entities such that outlier suppression and noise reduction is achieved simultaneously. In addition, we evaluate those hypotheses using a CRF which considers both the compatibility of individual objects under global 3D geometric context and the compatibility between adjacent objects under local 3D geometric context. Experiment results show that our approach compares favorably with the state of the art.
Unsupervised Visual Domain Adaptation Using Subspace Alignment
Authors: Basura Fernando, Amaury Habrard, Marc Sebban, Tinne Tuytelaars
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

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 subspace 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 hyperparameter corresponding to the size of the subspaces. We run our method on various datasets and show that, despite its intrinsic simplicity, it outperforms state of the art DA methods.
Semi-supervised Learning for Large Scale Image Cosegmentation
Authors: Zhengxiang Wang, Rujie Liu
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: This paper introduces to use semi-supervised learning for large scale image cosegmentation. Different from traditional 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 between 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 cosegmentation. For semi-supervised cosegmentation in large scale, we propose an effective method by minimizing an energy function, which consists of the inter-image distance, the intraimage distance and the balance term. We also propose an iterative updating algorithm to efficiently solve this energy function, which decomposes the original energy minimization problem into sub-problems, and updates each image alternatively to reduce the number of variables in each subproblem 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 segmentation, especially when the training data is limited.
Mining Multiple Queries for Image Retrieval: On-the-Fly Learning of an Object-Specific Mid-level Representation
Authors: Basura Fernando, Tinne Tuytelaars
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: In this paper we present a new method for object retrieval starting from multiple query images. The use of multiple 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 configurations. 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.
Event Detection in Complex Scenes Using Interval Temporal Constraints
Authors: Yifan Zhang, Qiang Ji, Hanqing Lu
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: In complex scenes with multiple atomic events happening sequentially or in parallel, detecting each individual event separately may not always obtain robust and reliable 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 computer vision techniques. In this paper, we propose an interval temporal constrained dynamic Bayesian network to extend Allen's interval algebra network (IAN) [2] from a deterministic 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 correlated 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.
Orderless Tracking through Model-Averaged Posterior Estimation
Authors: Seunghoon Hong, Suha Kwak, Bohyung Han
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: We propose a novel offline tracking algorithm based on model-averaged posterior estimation through patch matching across frames. Contrary to existing online and offline tracking methods, our algorithm is not based on temporallyordered estimates of target state but attempts to select easyto-track frames first out of the remaining ones without exploiting 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 density propagation across frames is implemented by an efficient patch matching technique, which is useful for our algorithm since it does not require motion smoothness assumption. 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 algorithm is conceptually well-suited for the sequences with abrupt motion, shot changes, and occlusion. We compare our tracking algorithm with existing techniques in real videos with such challenges and illustrate its superior performance qualitatively and quantitatively.
Log-Euclidean Kernels for Sparse Representation and Dictionary Learning
Authors: Peihua Li, Qilong Wang, Wangmeng Zuo, Lei Zhang
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: The symmetric positive desnite (SPD) matrices have been widely used in image and vision problems. Recently there are growing interests in studying sparse representation (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 manifold, existing work fails to take full advantage of its geometric 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 multiplication and scalar logarithmic multiplication desned in the Log-Euclidean framework, is a complete inner product space. We can thus develop a broad family of kernels that satisses Mercer's condition. These kernels characterize the geodesic distance and can be computed efsciently. 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 evaluated with various vision problems and shows notable performance gains over state-of-the-arts.
A Rotational Stereo Model Based on XSlit Imaging
Authors: Jinwei Ye, Yu Ji, Jingyi Yu
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Traditional stereo matching assumes perspective viewing cameras under a translational motion: the second camera is translated away from the first one to create parallax. In this paper, we investigate a different, rotational stereo 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 solution. 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.