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Compressed Hashing

Compressed Hashing

引用

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

作者： Lin, Yue Jin, Rong Cai, Deng Yan, Shuicheng Li, Xuelong Zhejiang Univ Coll Comp Sci State Key Lab CAD&CG Hangzhou 310003 Zhejiang Peoples R China Michigan State Univ Dept Comp Sci & Engn E Lansing MI 48824 USA Chinese Acad Sci OPTical IMagery Analy & Learn Beijing 100864 Peoples R China Natl Univ Singapore Dept Elect & Elect Engn Singapore 117548 Singapore

ISBN: (纸本)9780769549897

Recent studies have shown that hashing methods are effective for high dimensional nearest neighbor search. A common problem shared by many existing hashing methods is that in order to achieve a satisfied performance, a large number of hash tables (i.e., long code-words) are required. To address this challenge, in this paper we propose a novel approach called Compressed Hashing by exploring the techniques of sparse coding and compressed sensing. In particular, we introduce a sparse coding scheme, based on the approximation theory of integral operator, that generate sparse representation for high dimensional vectors. We then project sparse codes into a low dimensional space by effectively exploring the Restricted Isometry Property (RIP), a key property in compressed sensing theory. Both of the theoretical analysis and the empirical studies on two large data sets show that the proposed approach is more effective than the state-of-the-art hashing algorithms.

关键词： approximation theory compressed sensing data compression file organisation vectors

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Fusing Robust Face Region Descriptors via Multiple Metric Learning for Face recognition in the Wild

Fusing Robust Face Region Descriptors via Multiple Metric Le...

引用

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

作者： Cui, Zhen Li, Wen Xu, Dong Shan, Shiguang Chen, Xilin Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing Peoples R China Nanyang Technol Univ Sch Comp Engn Singapore Singapore Huaqiao Univ Sch Comp Sci Xiamen Peoples R China

ISBN: (纸本)9780769549897

In many real-world face recognition scenarios, face images can hardly be aligned accurately due to complex appearance variations or low-quality images. To address this issue, we propose a new approach to extract robust face region descriptors. Specifically, we divide each image (resp. video) into several spatial blocks (resp. spatial-temporal volumes) and then represent each block (resp. volume) by sum-pooling the nonnegative sparse codes of position-free patches sampled within the block (resp. volume). Whitened Principal Component Analysis (WPCA) is further utilized to reduce the feature dimension, which leads to our Spatial Face Region Descriptor (SFRD) (resp. Spatial-Temporal Face Region Descriptor, STFRD) for images (resp. videos). Moreover, we develop a new distance metric learning method for face verification called Pairwise-constrained Multiple Metric Learning (PMML) to effectively integrate the face region descriptors of all blocks (resp. volumes) from an image (resp. a video). Our work achieves the state-of-the-art performances on two real-world datasets LFW and YouTube Faces (YTF) according to the restricted protocol.

关键词： face recognition learning (artificial intelligence) principal component analysis

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Robust Canonical Time Warping for the Alignment of Grossly Corrupted Sequences

Robust Canonical Time Warping for the Alignment of Grossly C...

引用

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

作者： Panagakis, Yannis Nicolaou, Mihalis A. Zafeiriou, Stefanos Pantic, Maja Univ London Imperial Coll Sci Technol & Med Dept Comp London SW7 2AZ England

ISBN: (纸本)9780769549897

Temporal alignment of human behaviour from visual data is a very challenging problem due to a numerous reasons, including possible large temporal scale differences, inter/intra subject variability and, more importantly, due to the presence of gross errors and outliers. Gross errors are often in abundance due to incorrect localization and tracking, presence of partial occlusion etc. Furthermore, such errors rarely follow a Gaussian distribution, which is the de-facto assumption in machine learning methods. In this paper, building on recent advances on rank minimization and compressive sensing, a novel, robust to gross errors temporal alignment method is proposed. While previous approaches combine the dynamic time warping (DTW) with low-dimensional projections that maximally correlate two sequences, we aim to learn two underlying projection matrices ( one for each sequence), which not only maximally correlate the sequences but, at the same time, efficiently remove the possible corruptions in any datum in the sequences. the projections are obtained by minimizing the weighted sum of nuclear and l(1) norms, by solving a sequence of convex optimization problems, while the temporal alignment is found by applying the DTW in an alternating fashion. the superiority of the proposed method against the state-of-the-art time alignment methods, namely the canonical time warping and the generalized time warping, is indicated by the experimental results on both synthetic and real datasets.

关键词： Gaussian distribution computer vision convex programming data visualisation image sequences learning (artificial intelligence) matrix algebra minimisation

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3D R Transform on Spatio-Temporal Interest Points for Action recognition

3D <i>R</i> Transform on Spatio-Temporal Interest Points for...

引用

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

作者： Yuan, Chunfeng Li, Xi Hu, Weiming Ling, Haibin Maybank, Stephen Natl Lab Pattern Recognit Inst Automat Beijing Peoples R China Univ Adelaide Sch Comp Sci Adelaide SA 5005 Australia Temple Univ Dept Comp & Informat Sci Philadelphia PA 19122 USA Birkbeck Coll Sch Comp Sci & Informat Syst London WC1E 7HX England

ISBN: (纸本)9780769549897

Spatio-temporal interest points serve as an elementary building block in many modern action recognition algorithms, and most of them exploit the local spatio-temporal volume features using a Bag of Visual Words (BOVW) representation. Such representation, however, ignores potentially valuable information about the global spatio-temporal distribution of interest points. In this paper, we propose a new global feature to capture the detailed geometrical distribution of interest points. It is calculated by using the R transform which is defined as an extended 3D discrete Radon transform, followed by applying a two-directional two-dimensional principal component analysis. Such R feature captures the geometrical information of the interest points and keeps invariant to geometry transformation and robust to noise. In addition, we propose a new fusion strategy to combine the R feature with the BOVW representation for further improving recognition accuracy. We utilize a context-aware fusion method to capture both the pairwise similarities and higher-order contextual interactions of the videos. Experimental results on several publicly available datasets demonstrate the effectiveness of the proposed approach for action recognition.

关键词： Radon transforms computational geometry feature extraction image motion analysis image representation

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Semantic pixel sets based local binary patterns for face recognition

Semantic pixel sets based local binary patterns for face rec...

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11th Asian conference on computer vision, ACCV 2012

作者： Chai, Zhenhua Mendez-Vazquez, Heydi He, Ran Sun, Zhenan Tan, Tieniu National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Science P.O. Box 2728 Beijing 100080 China Advanced Technologies Application Center 7th Avenue #21812 b/ 218 and 222 P.C. 12200 Playa Havana Cuba

ISBN: (纸本)9783642374432

Feature extraction plays an important role in face recognition. Based on local binary patterns (LBP), we propose a novel face representation method which obtains histograms of semantic pixel sets based LBP (spsLBP) with a robust code voting (rcv). By clustering according the semantic pixel relations before the histogram estimation, the spsLBP makes better use of the spatial information over the original LBP. In this paper, we use a simple rule to use the semantic information. We cluster by the pixel intensity values, which is also invariant to monotonic grayscale changes, and it is in particular very useful when there are occlusions and expression variations on face images. Besides, the proposed representation adopts a new code voting strategy for LBP histogram computation, which makes it more robust. the proposed method is evaluated on three widely used face recognition databases: AR, FERET and LFW. Experimental results show that the proposed method can outperform the original uniform LBP and its extensions. © 2013 Springer-Verlag.

关键词： Local binary pattern

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A transductive transfer learning method for ship target recognition

A transductive transfer learning method for ship target reco...

引用

2013 7th International conference on Image and Graphics, ICIG 2013

作者： Dan, Zhiping Sang, Nong Wang, Ruolin Chen, Yanfei Chen, Xi Institute for Pattern Recognitionand Artificial Intelligence Huazhong University of Science and Technology Wuhan China School of Civil Engineering Wuhan University Wuhan China Institute of Intelligent Vision and Image Information China Three Gorges University Yichang China

ISBN: (纸本)9780769550503

Ship target recognition in infrared image remains a difficult problem, due to the projection or silhouette of a three-dimensional ship target being variable in shape, orientation and scale to make its recognizability unstable. In this paper, a transductive transfer learning framework is proposed to solve the problem. Hu moments is firstly extracted as feature vectors of target. then the transductive transfer learning method is used to find the common parameters between the feature spaces of the training ship samples and the detected ship targets, and transfer the similar knowledge from those data with different distributions. According to the experiment result in simulation infrared images, it shows that the ship targets can be recognized highly and reliably by our proposed framework. It demonstrates the robustness and effectiveness of our method for infrared images. © 2013 IEEE.

关键词： Infrared imaging

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Discriminative spatial codebook generation for image classification

Discriminative spatial codebook generation for image classif...

引用

2013 7th International conference on Image and Graphics, ICIG 2013

作者： Zhang, Chunjie Xiong, Wei Liu, Jing Fang, Liang Zhang, Weigang Huang, Qingming School of Computer and Control Engineering University of Chinese Academy of Sciences 100049 Beijing China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences P.O. Box 2728 Beijing China School of Civil Engineering Suzhou University of Science and Technology Suzhou 215011 China School of Computer Science and Technology Harbin Institute of Technology at Weihai Weihai 264209 China

ISBN: (纸本)9780769550503

Codebook plays an important role in the bag-of-visual-words (BoW) model for image classification. However, the traditional codebook generation procedure ignores the spatial information. Although a lot of works have been done to consider the spatial information for codebook generation, most of them rely on fixed region selection or partition of images, hence are not able to cope with the variations of images. To solve this problem, in this paper, we propose a novel discriminative spatial coding algorithm which can automatically generate and select the most representative codebooks for image representation. this is achieved by first generate a number of spatial codebooks through over-complete image partition with overlap. Second, for each local feature to be encoded, the most discriminative codebook is selected by jointly minimizing the encoding error and the codebook's spatial distance. Experimental results on several public image datasets show the effectiveness of the proposed discriminative spatial coding method for efficient image classification. © 2013 IEEE.

关键词： Image classification

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Robust feature encoding with neighborhood information for image classification

Robust feature encoding with neighborhood information for im...

引用

2013 7th International conference on Image and Graphics, ICIG 2013

作者： Liu, Bingyuan Liu, Jing Zhang, Chunjie Chen, Maolin Lu, Hanqing National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences China School of Computer and Control Engineering University of Chinese Academy of Sciences China Huawei Technologies China

ISBN: (纸本)9780769550503

the bag of visual words (BoW) model is one of the most successful model in image classification task. However, the major problem of the BoW model lies in the determination of visual words, which consists of codebook training and feature encoding phases. the traditional K-means and hard-assignment method completely ignore the structure of the local feature space, leading to high loss of information. To alleviate the information loss, we propose to incorporate the neighborhood information of the features into the codebook training and feature encoding process. We firstly propose a model to roughly measure the influence of the distribution of the neighboring features. then we combine the proposed model with the traditional K-means method in a probability perspective to train the visual codebook. Finally, in the feature encoding phase, both the hard-assignment and soft-assignment method are improved with the proposed neighborhood information term. We investigate our method on two popular datasets: 15-Scenes and Caltech-101. Experimental results demonstrate the effectiveness of our proposed method. © 2013 IEEE.

关键词： Image classification

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Labeling Examples that Matter: Relevance-Based Active Learning with Gaussian Processes 1

引用

35th German conference on pattern recognition (GCPR)

作者： Freytag, Alexander Rodner, Erik Bodesheim, Paul Denzler, Joachim Univ Jena Comp Vision Grp D-07745 Jena Germany

ISBN: (数字)9783642406027

ISBN: (纸本)9783642406010;9783642406027

Active learning is an essential tool to reduce manual annotation costs in the presence of large amounts of unsupervised data. In this paper, we introduce new active learning methods based on measuring the impact of a new example on the current model. this is done by deriving model changes of Gaussian process models in closed form. Furthermore, we study typical pitfalls in active learning and show that our methods automatically balance between the exploitation and the exploration trade-off. Experiments are performed with established benchmark datasets for visual object recognition and show that our new active learning techniques are able to outperform state-of-the-art methods.

关键词： Gaussian noise (electronic)

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Pedestrian Path Prediction with Recursive Bayesian Filters: A Comparative Study 1

引用

35th German conference on pattern recognition (GCPR)

作者： Schneider, Nicolas Gavrila, Dariu M. Environm Percept Daimler R&D Ulm Germany

ISBN: (数字)9783642406027

ISBN: (纸本)9783642406010;9783642406027

In the context of intelligent vehicles, we perform a comparative study on recursive Bayesian filters for pedestrian path prediction at short time horizons (< 2s). We consider Extended Kalman Filters (EKF) based on single dynamical models and Interacting Multiple Models (IMM) combining several such basic models (constant velocity/acceleration/ turn). these are applied to four typical pedestrian motion types (crossing, stopping, bending in, starting). Position measurements are provided by an external state-of-the-art stereo vision-based pedestrian detector. We investigate the accuracy of position estimation and path prediction, and the benefit of the IMMs vs. the simpler single dynamical models. Special care is given to the proper sensor modeling and parameter optimization. the dataset and evaluation framework are made public to facilitate benchmarking.

关键词： Stereo vision

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