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Locally Aligned Feature Transforms across Views

Locally Aligned Feature Transforms across Views

引用

26th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Li, Wei Wang, Xiaogang Chinese Univ Hong Kong Dept Elect Engn Shatin Hong Kong Peoples R China

ISBN: (纸本)9780769549897

In this paper, we propose a new approach for matching images observed in different camera views with complex cross-view transforms and apply it to person re-identification. It jointly partitions the image spaces of two camera views into different configurations according to the similarity of cross-view transforms. the visual features of an image pair from different views are first locally aligned by being projected to a common feature space and then matched with softly assigned metrics which are locally optimized. the features optimal for recognizing identities are different from those for clustering cross-view transforms. they are jointly learned by utilizing sparsity-inducing norm and information theoretical regularization. this approach can be generalized to the settings where test images are from new camera views, not the same as those in the training set. Extensive experiments are conducted on public datasets and our own dataset. Comparisons with the state-of-the-art metric learning and person re-identification methods show the superior performance of our approach.

关键词： feature extraction image matching information theory learning (artificial intelligence)

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Multi-source Deep Learning for Human Pose Estimation 27

Multi-source Deep Learning for Human Pose Estimation

引用

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

作者： Ouyang, Wanli Chu, Xiao Wang, Xiaogang Chinese Univ Hong Kong Dept Elect Engn Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781479951178

Visual appearance score, appearance mixture type and deformation are three important information sources for human pose estimation. this paper proposes to build a multi-source deep model in order to extract non-linear representation from these different aspects of information sources. With the deep model, the global, high-order human body articulation patterns in these information sources are extracted for pose estimation. the task for estimating body locations and the task for human detection are jointly learned using a unified deep model. the proposed approach can be viewed as a post-processing of pose estimation results and can flexibly integrate with existing methods by taking their information sources as input. By extracting the non-linear representation from multiple information sources, the deep model outperforms state-of-the-art by up to 8.6 percent on three public benchmark datasets.

关键词： Deep learning deep blief net deep model neural net work pose estimation pose estimation Information source Human

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Unsupervised Salience Learning for Person Re-identification

Unsupervised Salience Learning for Person Re-identification

引用

26th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Zhao, Rui Ouyang, Wanli Wang, Xiaogang Chinese Univ Hong Kong Dept Elect Engn Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9780769549897

Human eyes can recognize person identities based on some small salient regions. However, such valuable salient information is often hidden when computing similarities of images with existing approaches. Moreover, many existing approaches learn discriminative features and handle drastic viewpoint change in a supervised way and require labeling new training data for a different pair of camera views. In this paper, we propose a novel perspective for person re-identification based on unsupervised salience learning. Distinctive features are extracted without requiring identity labels in the training procedure. First, we apply adjacency constrained patch matching to build dense correspondence between image pairs, which shows effectiveness in handling misalignment caused by large viewpoint and pose variations. Second, we learn human salience in an unsupervised manner. To improve the performance of person re-identification, human salience is incorporated in patch matching to find reliable and discriminative matched patches. the effectiveness of our approach is validated on the widely used VIPeR dataset and EthZ dataset.

关键词： feature extraction image matching learning (artificial intelligence)

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Realtime and Robust Hand Tracking from Depth 27

Realtime and Robust Hand Tracking from Depth

引用

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

作者： Qian, Chen Sun, Xiao Wei, Yichen Tang, Xiaoou Sun, Jian Microsoft Res Redmond WA 98052 USA Chinese Univ Hong Kong Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781479951178

We present a realtime hand tracking system using a depth sensor. It tracks a fully articulated hand under large viewpoints in realtime (25 FPS on a desktop without using a GPU) and with high accuracy (error below 10 mm). To our knowledge, it is the first system that achieves such robustness, accuracy, and speed simultaneously, as verified on challenging real data. Our system is made of several novel techniques. We model a hand simply using a number of spheres and define a fast cost function. those are critical for realtime performance. We propose a hybrid method that combines gradient based and stochastic optimization methods to achieve fast convergence and good accuracy. We present new finger detection and hand initialization methods that greatly enhance the robustness of tracking.

关键词： ICP PSO hand tracking parametric subharmonic oscillator Inductively coupled plasma Iterative closest point algorithm Hand tracking Optimization methods Robustness

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Local Sparse Discriminant Analysis For Robust Face recognition

Local Sparse Discriminant Analysis For Robust Face Recogniti...

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26th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Kang, Cuicui Liao, Shengcai Xiang, Shiming Pan, Chunhong Chinese Acad Sci Inst Automat Beijing 100864 Peoples R China

ISBN: (纸本)9780769549903

the Linear Discriminant Analysis (LDA) algorithm plays an important role in pattern recognition. A common practice is that LDA and many of its variants generally learn dense bases, which are not robust to local image distortions and partial occlusions. Recently, the LASSO penalty has been incorporated into LDA to learn sparse bases. However, since the learned sparse coefficients are globally distributed all over the basis image, the solution is still not robust to partial occlusions. In this paper, we propose a Local Sparse Discriminant Analysis (LoSDA) method, which aims at learning discriminant bases that consist of local object parts. In this way, it is more robust than dense or global basis based LDA algorithms for visual classification. the proposed model is formulated as a constrained least square regression problem with a group sparse regularization. Furthermore, we derive a weighted LoSDA (WLoS-DA) approach to learn localized basis images, which also enables multi subspace learning and fusion. Finally, we develop an algorithm based on the Accelerated Proximal Gradient (APG) technique to solve the resulting weighted group sparse optimization problem. Experimental results on the FRGC v2.0 and the AR face databases show that the proposed LoSDA and WLoSDA algorithms both outperform the other state-of-the-art discriminant subspace learning algorithms under illumination variations and occlusions.

关键词： computer vision face recognition gradient methods image classification image fusion learning (artificial intelligence) least squares approximations optimisation regression analysis

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Continuous Action recognition and Segmentation in Untrimmed Videos 24

Continuous Action Recognition and Segmentation in Untrimmed ...

引用

24th International conference on pattern recognition (ICPR)

作者： Bai, Ruibin Zhao, Qing Zhou, Sanping Li, Yubing Zhao, Xueji Wang, Jinjun Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian Shaanxi Peoples R China 1 Tanhu 2nd Rd Wuhan Hubei Peoples R China Xi An Jiao Tong Univ Sch Elect & Informat Engn Xian Shaanxi Peoples R China Southwest Univ Chongqing Peoples R China

ISBN: (纸本)9781538637883

Recognizing continuous human action is a fundamental task in many real-world computer vision applications including video surveillance, video retrieval, and human-computer interaction, etc. It requires to recognize each action performed as well as their segmentation boundaries in a continuous sequence. In previous works, great progress has been reported for single action recognition, by using deep convolutional networks. In order to further improve the performance for continuous action recognition, in this paper, we introduce a discriminative approach consisting of three modules. the first feature extraction module uses a two stream Convolutional Neural Network to capture the appearance and the short-term motion information from the raw video input. Based on the obtained features, the second classification module performs spatial and temporal recognition and then fuses the two scores from respective feature stream. In the final segmentation module, a semi-Markov Conditional Field model, capable of handling long-term action interactions, is built to partition the action sequence. As can be seen in the experimental results, our approach obtains state-of-the-art performance on public datasets including 50Salads, Breakfast, and MERL Shopping. We have also visualized the continuous actions segmentation results for more insightful discussion in the paper.

关键词： Videos Feature extraction Motion segmentation Hidden Markov models pattern recognition Task analysis computer vision

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Position Determines Perspective: Investigating Perspective Distortion for Image Forensics of Faces 30

Position Determines Perspective: Investigating Perspective D...

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30th IEEE/CVF conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Peng, Bo Wang, Wei Dong, Jing Tan, Tieniu Chinese Acad Sci Inst Automat Ctr Res Intelligent Percept & Comp Beijing Peoples R China State Key Lab Cryptol Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China State Key Lab Informat Secur Beijing Peoples R China

ISBN: (纸本)9781538607336

this paper points out a new telltale trace - the characteristic of perspective distortion (CPD), for the image forensics of faces. the perspective distortion is determined by the position of image shooting, and it is often overlooked when creating a forgery, which results in the inconsistency between the claimed camera parameters and the CPD in the face image. To investigate this consistency problem, we cast it to the consistency between the claimed camera intrinsic parameters and the estimated ones from the CPD. Our parameter estimation approach is based on geometric observations that are related to CPD, like facial landmarks and contours. We analyze the estimation uncertainty caused by indeterminacy of observation to obtain a more reliable forensic decision. Experiments on synthetic datasets and real forgery examples demonstrate the effectiveness of the proposed method.

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks Signal processing algorithms

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Discover and Learn New Objects from Documentaries 30

Discover and Learn New Objects from Documentaries

引用

30th IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Chen, Kai Song, Hang Loy, Chen Change Lin, Dahua Chinese Univ Hong Kong Dept Informat Engn Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781538604571

Despite the remarkable progress in recent years, detecting objects in a new context remains a challenging task. Detectors learned from a public dataset can only work with a fixed list of categories, while training from scratch usually requires a large amount of training data with detailed annotations. this work aims to explore a novel approach - learning object detectors from documentary films in a weakly supervised manner. this is inspired by the observation that documentaries often provide dedicated exposition of certain object categories, where visual presentations are aligned with subtitles. We believe that object detectors can be learned from such a rich source of information. Towards this goal, we develop a joint probabilistic framework, where individual pieces of information, including video frames and subtitles, are brought together via both visual and linguistic links. On top of this formulation, we further derive a weakly supervised learning algorithm, where object model learning and training set mining are unified in an optimization procedure. Experimental results on a real world dataset demonstrate that this is an effective approach to learning new object detectors.

关键词： Detectors Visualization Pragmatics Training Proposals Optimization

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A Graph Regularized Deep Neural Network for Unsupervised Image Representation Learning 30

A Graph Regularized Deep Neural Network for Unsupervised Ima...

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30th IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Yang, Shijie Li, Liang Wang, Shuhui Zhang, Weigang Huang, Qingming Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing 100190 Peoples R China Harbin Inst Technol Sch Comp Sci & Technol Weihai 264209 Peoples R China

ISBN: (纸本)9781538604571

Deep Auto-Encoder (DAE) has shown its promising power in high-level representation learning. From the perspective of manifold learning, we propose a graph regularized deep neural network (GR-DNN) to endue traditional DAEs with the ability of retaining local geometric structure. A deep-structured regularizer is formulated upon multi-layer perceptions to capture this structure. the robust and discriminative embedding space is learned to simultaneously preserve the high-level semantics and the geometric structure within local manifold tangent space. theoretical analysis presents the close relationship between the proposed graph regularizer and the graph Laplacian regularizer in terms of the optimization objective. We also alleviate the growth of the network complexity by introducing the anchor-based bipartite graph, which guarantees the good scalability for large scale data. the experiments on four datasets show the comparable results of the proposed GR-DNN with the state-of-the-art methods.

关键词： geometrical construction Line graph image representation Bipartite graph Neural network Manifolds Perspective

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Representing Relative Visual Attributes with a Reference-Point-Based Decision Model 24

Representing Relative Visual Attributes with a Reference-Poi...

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24th International conference on pattern recognition (ICPR)

作者： Law, Marc T. Weng, Paul Univ Toronto Toronto ON Canada Shanghai Jiao Tong Univ Shanghai Peoples R China Univ Michigan Shanghai Jiao Tong Univ Joint Inst Shanghai Peoples R China

ISBN: (纸本)9781538637883

In many artificial intelligence, machine learning and computer vision tasks, the weighted sum model is used to value objects and define an order over them. In this paper, we consider two decision criteria defined as the (Euclidean and more generally Mahalanobis-like) distance to a reference point and investigate how they relate to the weighted sum model. In particular, we show that the distance-based representations can be seen as a relaxation of the representation induced by the weighted sum and we provide a characterization of the latter model with the former models in the case of strict orders. To illustrate our point, we consider the context of relative visual attributes. Nonetheless, our results also apply to other domains. More specifically, we present how these reference-point-based representations can be learned from pairwise comparisons and how they can be exploited for classification. Our experimental results show that those two criteria yield a more precise representation of the relative ordering for some attributes and that combining the best representations for each attribute improves recognition performance.

关键词： Visualization Euclidean distance Decision theory computer vision Computational modeling Machine learning Semantics

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