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ReD-SFA: Relation discovery based slow feature analysis for trajectory clustering

ReD-SFA: Relation discovery based slow feature analysis for ...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Zhang, Zhang Huang, Kaiqi Tan, Tieniu Yang, Peipei Li, Jun CRIPAC NLPR Institute of Automation Chinese Academy of Sciences China Centre for Quantum Computation and Intelligent Systems University of Technology Sydney Australia

ISBN: (纸本)9781467388511

For spectral embedding/clustering, it is still an open problem on how to construct an relation graph to reflect the intrinsic structures in data. In this paper, we proposed an approach, named Relation Discovery based Slow Feature Analysis (ReD-SFA), for feature learning and graph construction simultaneously. Given an initial graph with only a few nearest but most reliable pairwise relations, new reliable relations are discovered by an assumption of reliability preservation, i.e., the reliable relations will preserve their reliabilities in the learnt projection subspace. We formulate the idea as a cross entropy (CE) minimization problem to reduce the discrepancy between two Bernoulli distributions parameterized by the updated distances and the existing relation graph respectively. Furthermore, to overcome the imbalanced distribution of samples, a Boosting-like strategy is proposed to balance the discovered relations over all clusters. To evaluate the proposed method, extensive experiments are performed with various trajectory clustering tasks, including motion segmentation, time series clustering and crowd detection. The results demonstrate that ReD-SFA can discover reliable intra-cluster relations with high precision, and competitive clustering performance can be achieved in comparison with state-of-the-art.

关键词： pattern recognition

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Learning reconstruction-based remote gaze estimation

Learning reconstruction-based remote gaze estimation

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Yu, Pei Zhou, Jiahuan Wu, Ying Northwestern University 2145 Sheridan Road EvanstonIL60208 United States

ISBN: (纸本)9781467388511

It is a challenging problem to accurately estimate gazes from low-resolution eye images that do not provide fine and detailed features for eyes. Existing methods attempt to establish the mapping between the visual appearance space to the gaze space. Different from the direct regression approach, the reconstruction-based approach represents appearance and gaze via local linear reconstruction in their own spaces. A common treatment is to use the same local reconstruction in the two spaces, i.e., the reconstruction weights in the appearance space are transferred to the gaze space for gaze reconstruction. However, this questionable treatment is taken for granted but has never been justified, leading to significant errors in gaze estimation. This paper is focused on the study of this fundamental issue. It shows that the distance metric in the appearance space needs to be adjusted, before the same reconstruction can be used. A novel method is proposed to learn the metric, such that the affinity structure of the appearance space under this new metric is as close as possible to the affinity structure of the gaze space under the normal Euclidean metric. Furthermore, the local affinity structure invariance is utilized to further regularize the solution to the reconstruction weights, so as to obtain a more robust and accurate solution. Effectiveness of the proposed method is validated and demonstrated through extensive experiments on different subjects.

关键词： computer vision

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Multi-view people tracking via hierarchical trajectory composition?

Multi-view people tracking via hierarchical trajectory compo...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Xu, Yuanlu Liu, Xiaobai Liu, Yang Zhu, Song-Chun United States United States

ISBN: (纸本)9781467388511

This paper presents a hierarchical composition approach for multi-view object tracking. The key idea is to adaptively exploit multiple cues in both 2D and 3D, e.g., ground occupancy consistency, appearance similarity, motion coherence etc., which are mutually complementary while tracking the humans of interests over time. While feature online selection has been extensively studied in the past literature, it remains unclear how to effectively schedule these cues for the tracking purpose especially when encountering various challenges, e.g. occlusions, conjunctions, and appearance variations. To do so, we propose a hierarchical composition model and re-formulate multi-view multi-object tracking as a problem of compositional structure optimization. We setup a set of composition criteria, each of which corresponds to one particular cue. The hierarchical composition process is pursued by exploiting different criteria, which impose constraints between a graph node and its offsprings in the hierarchy. We learn the composition criteria using MLE on annotated data and efficiently construct the hierarchical graph by an iterative greedy pursuit algorithm. In the experiments, we demonstrate superior performance of our approach on three public datasets, one of which is newly created by us to test various challenges in multi-view multi-object tracking.

关键词： computer vision

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PSyCo: Manifold span reduction for super resolution

PSyCo: Manifold span reduction for super resolution

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Pérez-Pellitero, Eduardo Salvador, Jordi Ruiz-Hidalgo, Javier Rosenhahn, Bodo TNT Lab Leibniz Universität Hannover Germany Technicolor R and I Hannover Germany Image Processing Group Universitat Politècnica de Catalunya Spain

ISBN: (纸本)9781467388511

The main challenge in Super Resolution (SR) is to discover the mapping between the low- and high-resolution manifolds of image patches, a complex ill-posed problem which has recently been addressed through piecewise linear regression with promising results. In this paper we present a novel regression-based SR algorithm that benefits from an extended knowledge of the structure of both manifolds. We propose a transform that collapses the 16 variations induced from the dihedral group of transforms (i.e. rotations, vertical and horizontal reflections) and antipodality (i.e. diametrically opposed points in the unitary sphere) into a single primitive. The key idea of our transform is to study the different dihedral elements as a group of symmetries within the high-dimensional manifold. We obtain the respective set of mirror-symmetry axes by means of a frequency analysis of the dihedral elements, and we use them to collapse the redundant variability through a modified symmetry distance. The experimental validation of our algorithm shows the effectiveness of our approach, which obtains competitive quality with a dictionary of as little as 32 atoms (reducing other methods' dictionaries by at least a factor of 32) and further pushing the state-of-the-art with a 1024 atoms dictionary.

关键词： computer vision

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Learning a discriminative null space for person re-identification

Learning a discriminative null space for person re-identific...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Zhang, Li Xiang, Tao Gong, Shaogang Queen Mary University of London United Kingdom

ISBN: (纸本)9781467388511

Most existing person re-identification (re-id) methods focus on learning the optimal distance metrics across camera views. Typically a person's appearance is represented using features of thousands of dimensions, whilst only hundreds of training samples are available due to the difficulties in collecting matched training images. With the number of training samples much smaller than the feature dimension, the existing methods thus face the classic small sample size (SSS) problem and have to resort to dimensionality reduction techniques and/or matrix regularisation, which lead to loss of discriminative power. In this work, we propose to overcome the SSS problem in re-id distance metric learning by matching people in a discriminative null space of the training data. In this null space, images of the same person are collapsed into a single point thus minimising the within-class scatter to the extreme and maximising the relative between-class separation simultaneously. Importantly, it has a fixed dimension, a closed-form solution and is very efficient to compute. Extensive experiments carried out on five person re-identification benchmarks including VIPeR, PRID2011, CUHK01, CUHK03 and Market1501 show that such a simple approach beats the state-of-the-art alternatives, often by a big margin.

关键词： computer vision

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Solving small-piece jigsaw puzzles by growing consensus

Solving small-piece jigsaw puzzles by growing consensus

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Son, Kilho Moreno, Daniel Hays, James Cooper, David B. Brown University United States Georgia Institute of Technology United States

ISBN: (纸本)9781467388511

In this paper, we present a novel computational puzzle solver for square-piece image jigsaw puzzles with no prior information such as piece orientation, anchor pieces or resulting dimension of the puzzle. By "piece" we mean a square dxd block of pixels, where we investigate pieces as small as 7x7 pixels. To reconstruct such challenging puzzles, we aim to search for piece configurations which maximize the size of consensus (i.e. grid or loop) configurations which represent a geometric consensus or agreement among pieces. Pieces are considered for addition to the existing assemblies if these pieces increase the size of the consensus configurations. In contrast to previous puzzle solvers which goal for assemblies maximizing compatibility measures between all pairs of pieces and thus depend heavily on the pairwise compatibility measure used, our new approach reduces the dependency on the pairwise compatibility measures which become increasingly uninformative at small scales and instead exploits geometric agreement among pieces. Our contribution also includes an improved pairwise compatibility measure which exploits directional derivative information along adjoining boundaries of the pieces. For the challenging unknown orientation piece puzzles where the size of pieces is small, we reduce assembly error by up to 75% compared with previous algorithms for standard datasets.

关键词： computer vision

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Unsupervised learning from narrated instruction videos

Unsupervised learning from narrated instruction videos

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2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Alayrac, Jean-Baptiste Bojanowski, Piotr Agrawal, Nishant Sivic, Josef Laptev, Ivan Lacoste-Julien, Simon WILLOW Project-Team Département d'Informatique de l'Ecole Normale Supérieure ENS/INRIA/CNRS UMR 8548 Paris France SIERRA Project-Team Département d'Informatique de l'Ecole Normale Supérieure ENS/INRIA/CNRS UMR 8548 Paris France IIIT Hyderabad India

ISBN: (纸本)9781467388511

We address the problem of automatically learning the main steps to complete a certain task, such as changing a car tire, from a set of narrated instruction videos. The contributions of this paper are three-fold. First, we develop a new unsupervised learning approach that takes advantage of the complementary nature of the input video and the associated narration. The method solves two clustering problems, one in text and one in video, applied one after each other and linked by joint constraints to obtain a single coherent sequence of steps in both modalities. Second, we collect and annotate a new challenging dataset of real-world instruction videos from the Internet. The dataset contains about 800,000 frames for five different tasks1 that include complex interactions between people and objects, and are captured in a variety of indoor and outdoor settings. Third, we experimentally demonstrate that the proposed method can automatically discover, in an unsupervised manner, the main steps to achieve the task and locate the steps in the input videos.

关键词： computer vision

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Globally optimal rigid intensity based registration: A fast fourier domain approach

Globally optimal rigid intensity based registration: A fast ...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Nasihatkon, Behrooz Fejne, Frida Kahl, Fredrik Department of Signals and Systems Chalmers University of Technology Sweden Centre for Mathematical Sciences Lund University Sweden MedTech West Sweden

ISBN: (纸本)9781467388511

High computational cost is the main obstacle to adapting globally optimal branch-and-bound algorithms to intensity-based registration. Existing techniques to speed up such algorithms use a multiresolution pyramid of images and bounds on the target function among different resolutions for rigidly aligning two images. In this paper, we propose a dual algorithm in which the optimization is done in the Fourier domain, and multiple resolution levels are replaced by multiple frequency bands. The algorithm starts by computing the target function in lower frequency bands and keeps adding higher frequency bands until the current subregion is either rejected or divided into smaller areas in a branch and bound manner. Unlike spatial multiresolution approaches, to compute the target function for a wider frequency area, one just needs to compute the target in the residual bands. Therefore, if an area is to be discarded, it performs just enough computations required for the rejection. This property also enables us to use a rather large number of frequency bands compared to the limited number of resolution levels used in the space domain algorithm. Experimental results on real images demonstrate considerable speed gains over the space domain method in most cases.

关键词： computer vision

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A robust multilinear model learning framework for 3D faces

A robust multilinear model learning framework for 3D faces

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Bolkart, Timo Wuhrer, Stefanie Saarland University Germany Inria Grenoble Rhône-Alpes France

ISBN: (纸本)9781467388511

Multilinear models are widely used to represent the statistical variations of 3D human faces as they decouple shape changes due to identity and expression. Existing methods to learn a multilinear face model degrade if not every person is captured in every expression, if face scans are noisy or partially occluded, if expressions are erroneously labeled, or if the vertex correspondence is inaccurate. These limitations impose requirements on the training data that disqualify large amounts of available 3D face data from being usable to learn a multilinear model. To overcome this, we introduce the first framework to robustly learn a multilinear model from 3D face databases with missing data, corrupt data, wrong semantic correspondence, and inaccurate vertex correspondence. To achieve this robustness to erroneous training data, our framework jointly learns a multilinear model and fixes the data. We evaluate our framework on two publicly available 3D face databases, and show that our framework achieves a data completion accuracy that is comparable to state-of-the-art tensor completion methods. Our method reconstructs corrupt data more accurately than state-of-the-art methods, and improves the quality of the learned model significantly for erroneously labeled expressions.

关键词： computer vision

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Modality and component aware feature fusion for RGB-D scene classification

Modality and component aware feature fusion for RGB-D scene ...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Wang, Anran Cai, Jianfei Lu, Jiwen Cham, Tat-Jen School of Computer Engineering Nanyang Technological University Singapore Singapore Department of Automation Tsinghua University Beijing China

ISBN: (纸本)9781467388511

While convolutional neural networks (CNN) have been excellent for object recognition, the greater spatial variability in scene images typically meant that the standard full-image CNN features are suboptimal for scene classification. In this paper, we investigate a framework allowing greater spatial flexibility, in which the Fisher vector (FV) encoded distribution of local CNN features, obtained from a multitude of region proposals per image, is considered instead. The CNN features are computed from an augmented pixel-wise representation comprising multiple modalities of RGB, HHA and surface normals, as extracted from RGB-D data. More significantly, we make two postulates: (1) component sparsity - that only a small variety of region proposals and their corresponding FV GMM components contribute to scene discriminability, and (2) modal non-sparsity - within these discriminative components, all modalities have important contribution. In our framework, these are implemented through regularization terms applying group lasso to GMM components and exclusive group lasso across modalities. By learning and combining regressors for both proposal-based FV features and global CN-N features, we were able to achieve state-of-the-art scene classification performance on the SUNRGBD Dataset and NYU Depth Dataset V2.

关键词： Classification (of information)

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