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Human Action recognition Based on Context-Dependent Graph Kernels 27

Human Action Recognition Based on Context-Dependent Graph Ke...

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27th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Wu, Baoxin Yuan, Chunfeng Hu, Weiming Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing Peoples R China

ISBN: (纸本)9781479951178

Graphs are a powerful tool to model structured objects, but it is nontrivial to measure the similarity between two graphs. In this paper, we construct a two-graph model to represent human actions by recording the spatial and temporal relationships among local features. We also propose a novel family of context-dependent graph kernels (CGKs) to measure similarity between graphs. First, local features are used as the vertices of the two-graph model and the relationships among local features in the intra-frames and inter-frames are characterized by the edges. then, the proposed CGKs are applied to measure the similarity between actions represented by the two-graph model. Graphs can be decomposed into numbers of primary walk groups with different walk lengths and our CGKs are based on the context-dependent primary walk group matching. Taking advantage of the context information makes the correctly matched primary walk groups dominate in the CGKs and improves the performance of similarity measurement between graphs. Finally, a generalized multiple kernel learning algorithm with a proposed l12-norm regularization is applied to combine these CGKs optimally together and simultaneously train a set of action classifiers. We conduct a series of experiments on several public action datasets. Our approach achieves a comparable performance to the state-of-the-art approaches, which demonstrates the effectiveness of the two-graph model and the CGKs in recognizing human actions.

关键词： Human action recognition context-dependent graph kernels generalized multiple kernel learning two-graph model

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Saliency aggregation via hard-voting evolution

Saliency aggregation via hard-voting evolution

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2015 IEEE 12th International conference on Electronic Measurement & Instruments

作者： Zheng Ling Chen Shuhan Hu Xuelong School of Information Engineering Yangzhou University

Generic object level saliency detection is important for many vision tasks, such as object detection, compression, recognition, segmentation and so on.. A variety of saliency detection methods have been proposed in recently, which often complement each other. In order to combine them, we propose a Hard-voting Evolution saliency aggregation algorithm in this paper. Specially, it is consist of three stages. First, we integrate each individual map to get a reference map by linear summation. Second, each of the individual maps is updated in Bayes framework based on the obtained reference map. third, we use our proposed approach to get the aggregated saliency map. Finally, we do iterative operation to further improve performance. Experiments on three publicly available datasets demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods.

关键词： Saliency detection Saliency aggregation Hard-voting Evolution

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Multi-target Tracking with Motion Context in Tensor Power Iteration 27

Multi-target Tracking with Motion Context in Tensor Power It...

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27th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Shi, Xinchu Ling, Haibin Hu, Weiming Yuan, Chunfeng Xing, Junliang Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing Peoples R China Temple Univ Dept Comp & Informat Sci Philadelphia PA 19122 USA

ISBN: (纸本)9781479951178

Interactions between moving targets often provide discriminative clues for multiple target tracking (MTT), though many existing approaches ignore such interactions due to difficulty in effectively handling them. In this paper, we model interactions between neighbor targets by pair-wise motion context, and further encode such context into the global association optimization. To solve the resulting global non-convex maximization, we propose an effective and efficient power iteration framework. this solution enjoys two advantages for MTT: First, it allows us to combine the global energy accumulated from individual trajectories and the between-trajectory interaction energy into a united optimization, which can be solved by the proposed power iteration algorithm. Second, the framework is flexible to accommodate various types of pairwise context models and we in fact studied two different context models in this paper. For evaluation, we apply the proposed methods to four public datasets involving different challenging scenarios such as dense aerial borne traffic tracking, dense point set tracking, and semi-crowded pedestrian tracking. In all the experiments, our approaches demonstrate very promising results in comparison with state-of-the-art trackers.

关键词： Multi-target tracking motion context tensor power iteration

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Analysis on Urban Collective Call Behavior to Earthquake

Analysis on Urban Collective Call Behavior to Earthquake

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IEEE International conference on High Performance Computing and Communications (HPCC)

作者： Xiaoping Yu Tao Pei Keke Gai Li Guo College of Information Science and Technology Shihezi University Shihezi China State Key Laboratory of Resource and Environmental Information System Chinese Academy of Sciences Beijing China Department of Computer Science Pace University New York NY USA

ISBN: (纸本)9781479989386

Despite recent advances in uncovering the quantitative features of human activities in routine life, human call behavior in earthquake is still less clear. Taking the data of mobile phone records produced by the users in 3 days in the northwest of China, we systematically analyze the characteristics of mobile phone call patterns to earthquake by using ratio analysis and degree distribution method. We find that as a whole, earthquake brings about the significant growth in the indices of data volume, phone volume, duration, distant call volume and local call volume, etc. And the duration of the calls has a significant increase than that of the number of the calls. From the temporal perspective, we discover that at the first 2 hours that earthquake took place, people tend to make much more local calls rather than distant calls. However, unlike the local call, the big volume of distant calls last a whole day except for 3 hours in the afternoon. More interesting, although there is the biggest data volume and phone volume in the day that earthquake happened, only those who contact less than 10 phone number dominate the biggest data volume and phone volume. this demonstrates the pattern difference between the business calls and the private calls. the in-depth understanding of human behavior in emergency help us understand many complex socio-economic phenomena, and find applications in public opinion monitoring, disease control, transportation system design, calling center services, and information recommendation.

关键词： Earthquakes Mobile handsets Indexes Probability distribution Cities and towns Mobile communication Embedded systems

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Sparse Dictionary Learning for Edit Propagation of High-resolution Images 27

Sparse Dictionary Learning for Edit Propagation of High-reso...

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27th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Chen, Xiaowu Zou, Dongqing Li, Jianwei Cao, Xiaochun Zhao, Qinping Zhang, Hao Beihang Univ Sch Comp Sci & Engn State Key Lab Virtual Real Technol & Syst Beijing 100191 Peoples R China Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Simon Fraser Univ Burnaby BC V5A 1S6 Canada

ISBN: (纸本)9781479951178

We introduce a method of sparse dictionary learning for edit propagation of high-resolution images or video. Previous approaches for edit propagation typically employ a global optimization over the whole set of image pixels, incurring a prohibitively high memory and time consumption for high-resolution images. Rather than propagating an edit pixel by pixel, we follow the principle of sparse representation to obtain a compact set of representative samples (or features) and perform edit propagation on the samples instead. the sparse set of samples provides an intrinsic basis for an input image, and the coding coefficients capture the linear relationship between all pixels and the samples. the representative set of samples is then optimized by a novel scheme which maximizes the KL-divergence between each sample pair to remove redundant samples. We show several applications of sparsity-based edit propagation including video recoloring, theme editing, and seamless cloning, operating on both color and texture features. We demonstrate that with a sample-to-pixel ratio in the order of 0.01%, signifying a significant reduction on memory consumption, our method still maintains a high-degree of visual fidelity.

关键词： Pixels

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Adaptive Partial Differential Equation Learning for Visual Saliency Detection 27

Adaptive Partial Differential Equation Learning for Visual S...

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27th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Liu, Risheng Cao, Junjie Lin, Zhouchen Shan, Shiguang Dalian Univ Technol Dalian Peoples R China Peking Univ Key Lab Machine Percept MOE Beijing Peoples R China Chinese Acad Sci Key Lab Intelligent Informat Proc Beijing 100864 Peoples R China

ISBN: (纸本)9781479951178

Partial Differential Equations (PDEs) have been successful in solving many low-level vision tasks. However, it is a challenging task to directly utilize PDEs for visual saliency detection due to the difficulty in incorporating human perception and high-level priors to a PDE system. Instead of designing PDEs with fixed formulation and boundary condition, this paper proposes a novel framework for adaptively learning a PDE system from an image for visual saliency detection. We assume that the saliency of image elements can be carried out from the relevances to the saliency seeds (i.e., the most representative salient elements). In this view, a general Linear Elliptic System with Dirichlet boundary (LESD) is introduced to model the diffusion from seeds to other relevant points. For a given image, we first learn a guidance map to fuse human prior knowledge to the diffusion system. then by optimizing a discrete submodular function constrained with this LESD and a uniform matroid, the saliency seeds (i.e., boundary conditions) can be learnt for this image, thus achieving an optimal PDE system to model the evolution of visual saliency. Experimental results on various challenging image sets show the superiority of our proposed learning-based PDEs for visual saliency detection.

关键词： Learning-Based PDEs Saliency Detection Submodular Optimization

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Towards Multi-view and Partially-occluded Face Alignment 27

Towards Multi-view and Partially-occluded Face Alignment

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27th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Xing, Junliang Niu, Zhiheng Huang, Junshi Hu, Weiming Yan, Shuicheng Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100190 Peoples R China Natl Univ Singapore Dept Elect & Comp Engn Singapore 117576 Singapore NUS Beijing Peoples R China

ISBN: (纸本)9781479951178

We present a robust model to locate facial landmarks under different views and possibly severe occlusions. To build reliable relationships between face appearance and shape with large view variations, we propose to formulate face alignment as an l(1)-induced Stagewise Relational Dictionary (SRD) learning problem. During each training stage, the SRD model learns a relational dictionary to capture consistent relationships between face appearance and shape, which are respectively modeled by the pose-indexed image features and the shape displacements for current estimated landmarks. During testing, the SRD model automatically selects a sparse set of the most related shape displacements for the testing face and uses them to refine its shape iteratively. To locate facial landmarks under occlusions, we further propose to learn an occlusion dictionary to model different kinds of partial face occlusions. By deploying the occlusion dictionary into the SRD model, the alignment performance for occluded faces can be further improved. Our algorithm is simple, effective, and easy to implement. Extensive experiments on two benchmark datasets and two newly built datasets have demonstrated its superior performances over the state-of-the-art methods, especially for faces with large view variations and/or occlusions.

关键词： Face alignment dictionary learning sparse coding

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High-level spatial modeling in convolutional neural network with application to pedestrian detection

High-level spatial modeling in convolutional neural network ...

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Canadian conference on Electrical and computer Engineering (CCECE)

作者： Feng Liu Yongzhen Huang Wankou Yang Changyin Sun School of Automation. Southeast University Nanjing China Key Lab of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China Key Lab of Child Development and Learning Science of Ministry of Education. Southeast University Nanjing China

ISBN: (纸本)9781479958306

Convolutional neural network (CNN) has achieved great success in many vision tasks. A key to this success is its ability to powerful automatically learns both high-level and low-level features. In general, low-level features have a small size of receptive fields and appear multiple times in different locations of objects, while high-level semantic features have a relatively large size of receptive fields and only appear once in a specific location of objects. However, traditional CNN treats these two kinds of features in the same manner, i.e., learning them by the convolution operation, which can be approximately considered as cumulating the probabilities that a feature appears in different locations. this strategy is reasonable for low-level features but not for high-level semantic ones, especially in the case of pedestrian detection, where a local feature can be shared by different locations but a semantic part, e.g., a head, only appears once for a human. To jointly model the spatial structure and appearance of high-level semantic features, we propose a new module to learn spatially weighted max pooling in CNN. the proposed method is evaluated on several pedestrian detection databases and the experimental results show that it achieves much better performance than traditional CNN.

关键词： Convolution Semantics Kernel Deformable models Computational modeling Feature extraction Training

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Adaptive Object Retrieval with Kernel Reconstructive Hashing 27

Adaptive Object Retrieval with Kernel Reconstructive Hashing

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27th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Yang, Haichuan Bai, Xiao Zhou, Jun Ren, Peng Zhang, Zhihong Cheng, Jian Beihang Univ Sch Comp Sci & Engn Beijing 100191 Peoples R China Griffith Univ Sch Informat & Commun Technol Nathan Qld 4111 Australia China Univ Petr Coll Informat & Control Engn Beijing Peoples R China Xiamen Univ Software Sch Xiamen Peoples R China Chinese Acad Sci Inst Automat Beijing 100864 Peoples R China

ISBN: (纸本)9781479951178

Hashing is very useful for fast approximate similarity search on large database. In the unsupervised settings, most hashing methods aim at preserving the similarity defined by Euclidean distance. Hash codes generated by these approaches only keep their Hamming distance corresponding to the pairwise Euclidean distance, ignoring the local distribution of each data point. this objective does not hold for k-nearest neighbors search. In this paper, we firstly propose a new adaptive similarity measure which is consistent with k-NN search, and prove that it leads to a valid kernel. then we propose a hashing scheme which uses binary codes to preserve the kernel function. Using low-rank approximation, our hashing framework is more effective than existing methods that preserve similarity over arbitrary kernel. the proposed kernel function, hashing framework, and their combination have demonstrated significant advantages compared with several state-of-the-art methods.

关键词： Approximate nearest neighbor search adaptive similarity binary code binary code hashing Nearest neighbor query Seeds KERNEL FUNCTIONS Kernel Hamming distance

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DeepReID: Deep Filter Pairing Neural Network for Person Re-Identification 27

DeepReID: Deep Filter Pairing Neural Network for Person Re-I...

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27th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Li, Wei Zhao, Rui Xiao, Tong Wang, Xiaogang Chinese Univ Hong Kong Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781479951178

Person re-identification is to match pedestrian images from disjoint camera views detected by pedestrian detectors. Challenges are presented in the form of complex variations of lightings, poses, viewpoints, blurring effects, image resolutions, camera settings, occlusions and background clutter across camera views. In addition, misalignment introduced by the pedestrian detector will affect most existing person re-identification methods that use manually cropped pedestrian images and assume perfect detection. In this paper, we propose a novel filter pairing neural network (FPNN) to jointly handle misalignment, photometric and geometric transforms, occlusions and background clutter. All the key components are jointly optimized to maximize the strength of each component when cooperating with others. In contrast to existing works that use handcrafted features, our method automatically learns features optimal for the re-identification task from data. the learned filter pairs encode photometric transforms. Its deep architecture makes it possible to model a mixture of complex photometric and geometric transforms. We build the largest benchmark re-id dataset with 13,164 images of 1,360 pedestrians. Unlike existing datasets, which only provide manually cropped pedestrian images, our dataset provides automatically detected bounding boxes for evaluation close to practical applications. Our neural network significantly outperforms state-of-the-art methods on this dataset.

关键词： Person Re-Identification

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