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IEEE International Conference on Multimedia and Expo (ICME)

作者： Huiying Liu Shuqiang Jiang Qingming Huang Changsheng Xu Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Chinese Academy and Sciences Beijing China Chinese Academy of Sciences Beijing Beijing CN Institute for Infocomm Research Agency for Science Technology and Research Singapore

Virtual Content Insertion (VCI) is an emerging application of video analysis. For VCI the spatial position is very important as improper placement will make the insertion intrusive. To choose the spatial position, we propose the notation of Lower Attentive Region (LAR) and provide a generic framework of LAR detection for broadcast video. An LAR is defined, from the cognition point of view, as a region of the video frame which attracts less audiencepsilas attention. It can be changed with little interruption to the main content of the original video. The proposed LAR detection framework includes both bottom-up and top-down modules and can be adapted to all types of videos. Finally we apply the proposed LAR detection approach to broadcast sports video by integrating domain knowledge. The Experiments on LAR detection and VCI in broadcast video demonstrate the effectiveness of the proposed method.

关键词： Cameras Entropy Streaming media Visualization Multimedia communication Conferences information theory

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Unified Principal Component Analysis with Generalized Covariance Matrix for Face Recognition

Unified Principal Component Analysis with Generalized Covari...

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26th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2008), vol.1

作者： Shiguang Shan Bo Cao Yu Su Laiyun Qing Xilin Chen Wen Gao Key Laboratory of Intelligent Information Processing Chinese Academy and Sciences Beijing China Sigma Center Microsoft Advanced Technology Center Beijing China School of Computer Science and Technology Harbin Institute of Technology Harbin China Graduate School of Chinese Academy of Sciences Beijing China

Recently, 2DPCA and its variants have attracted much attention in face recognition area. In this paper, some efforts are made to discover the underlying fundaments of these methods, and a novel framework called Unified Principal Component Analysis (UPCA) is proposed. First, we introduce a novel concept, named Generalized Covariance Matrix (GCM), which is naturally derived from the traditional Covariance Matrix (CM). Each element of GCM is a generalized covariance of two random vectors rather than two scalar variables in CM. Based on GCM, the UPCA framework is proposed, from which the traditional PCA and its 2D counterparts can be deduced as special cases. Furthermore, under the UPCA framework, we not only revisit the existing 2D PCA methods and their limitations, but also propose two new methods: the grid-sampling method (GridPCA) and the intra-group correlation reduction method. Extensive experimental results on the FERET face database support the theoretical analysis and validate the feasibility of the proposed methods.

关键词： Principal component analysis Covariance matrix Face recognition Laboratories information processing Computer science Smart pixels Image representation Image databases Usability

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Point based large-scale crowds real-time 3D visualization method

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Jisuanji Yanjiu yu Fazhan/Computer Research and Development 2008年第10期45卷 1731-1738页

作者： Shu, Bo Mao, Tianlu Xu, Wenbin Wang, Zhaoqi Virtual Reality Laboratory Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Graduate University Chinese Academy of Sciences Beijing 100049 China

3D visualization of large-scale virtual crowds is a very important and interesting problem in research fields of virtual reality. For reasons of efficiency and visual realism, it is very difficult to populate, animate and render large-scale virtual crowds with hundreds of thousand individually animated virtual characters in real-time applications;especially for the scene which has more than ten thousands of individuals with different shapes and motions. In this paper, an efficient method to visualize large-scale virtual crowds is presented. Firstly, using model variation technique, many different models can be derived from a small number of model templates. Secondly, the crowds can be animated individually by deforming a small number of elements in motion database. Thirdly, using a developed point sample rendering algorithm, large-scale crowds can be displayed in real-time. This method can be used to visualize different dynamic crowds which require both real-time efficiency and large number of virtual individuals support. Based on this work, an efficient and readily usable 3D visualization system is presented. It can provide very high visual realism for large crowds' visualization in interactive frame rates on a regular PC. The 3D visualization of 60000 people evacuating from a building is also realized in real-time based on the system.

关键词： Virtual reality

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Method of adaptively selecting best LDA model based on density

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Jisuanji Xuebao/Chinese Journal of Computers 2008年第10期31卷 1780-1787页

作者： Cao, Juan Zhang, Yong-Dong Li, Jin-Tao Tang, Sheng Virtual Reality Laboratory Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Graduate University Chinese Academy of Sciences Beijing 100049 China

Topic models have been successfully used to information classification and retrieval. These models can capture word correlations in a collection of textual documents with a low-dimensional set of multinomial distribution, called "topics". It is important but difficult to select an appropriate number of topics for a specific dataset. This paper proposes a theorem that the model reaches optimum as the average similarity among topics reaches minimum, and based on this theorem, proposes a method of adaptively selecting the best LDA model based on density. Experiments show that the proposed method can achieve performance matching the best of LDA without manually tuning the number of topics.

关键词： Classification (of information)

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People re-detection using Adaboost with sift and color correlogram

People re-detection using Adaboost with sift and color corre...

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IEEE International Conference on Image processing

作者： Lei Hu Shuqiang Jiang Qingming Huang Wen Gao Chinese Academy of Sciences Beijing China Key Lab of Intelligent Information Processing Chinese Academy and Sciences Beijing China Chinese Academy of Sciences Beijing Beijing CN Institute of Computing Technology Chinese Academy and Sciences Beijing China Institute of Digital Media Peking University Beijing China

People re-detection aims at performing re-identification of people who leave the scene and reappear after some time. This is an important problem especially in video surveillance scenarios. In this paper, we present a method of people re-detection within the context of visual sequence in single-camera setup. We consider re-detection as a binary classification problem, where both global and local descriptors are employed for training strong classifier on-line with adaboost to distinguish a newly detected people as tracked or new occurrence. The strong classifier will be updated while match is ascertained. A predetermined classifier with well-chosen threshold is employed as assistant of training examples collection. We test the performance of our approach on 4 different scenes including 51 video sequences taken from the CAVIAR database and 4 video sequences shot by ourselves. The results show that our re-detection algorithm can robustly handle variations in illumination, pose, scale, and camera-view.

关键词： Cameras Layout Surveillance Color Video sequences Data mining Joining processes Histograms information processing Computers

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Secure and Incidental Distortion Tolerant Digital Signature for Image Authentication

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Journal of Computer Science & technology 2007年第4期22卷 618-625页

作者：张勇东唐胜李锦涛 Key Laboratory of Intelligent Information Institute of Computing TechnologyChinese Academy of Sciences

In this paper, a secure and incidental distortion tolerant signature method for image authentication is proposed. The generation of authentication signature is based on Hotelling＇s T-square Statistic （HTS） via Principal Component Analysis （PCA） of block DCT coefficients. HTS values of all blocks construct a unique and stable ＂block-edge image＂, i.e, Structural and Statistical Signature （SSS）. The characteristic of SSS is that it is short, and can tolerate contentpreserving manipulations while keeping sensitive to content-changing attacks, and locate tampering easily. During signature matching, the Fisher criterion is used to obtain optimal threshold for automatically and universally distinguishing incidental manipulations from malicious attacks. Moreover, the security of SSS is achieved by encryption of the DCT coefficients with chaotic sequences before PCA. Experiments show that the novel method is effective for authentication.

关键词： image authentication digital signature PCA （Principal Component Analysis） Hotelling＇s T-square statistic

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Manifold-Manifold Distance with Application to Face Recognition based on Image Set

Manifold-Manifold Distance with Application to Face Recognit...

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26th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2008), vol.9

作者： Ruiping Wang Shiguang Shan Xilin Chen Wen Gao Graduate School of Chinese Academy of Sciences Beijing China Digital Media Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing China Key Lab of Intelligent Information Processing Chinese Academy and Sciences Beijing China School of EE&CS Peking University Beijing China

In this paper, we address the problem of classifying image sets, each of which contains images belonging to the same class but covering large variations in, for instance, viewpoint and illumination. We innovatively formulate the problem as the computation of Manifold-Manifold Distance (MMD), i.e., calculating the distance between nonlinear manifolds each representing one image set. To compute MMD, we also propose a novel manifold learning approach, which expresses a manifold by a collection of local linear models, each depicted by a subspace. MMD is then converted to integrating the distances between pair of subspaces respectively from one of the involved manifolds. The proposed MMD method is evaluated on the task of Face Recognition based on Image Set (FRIS). In FRIS, each known subject is enrolled with a set of facial images and modeled as a gallery manifold, while a testing subject is modeled as a probe manifold, which is then matched against all the gallery manifolds by MMD. Identification is achieved by seeking the minimum MMD. Experimental results on two public face databases, Honda/UCSD and CMU MoBo, demonstrate that the proposed MMD method outperforms the competing methods.

关键词： Face recognition Probes Image converters Content addressable storage Testing Cameras Image storage Video sequences Image recognition Object recognition

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Research on rough set theory and applications in China

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Lecture Notes in Computer Science

作者： Wang, Guoyin Zhang, Qinghua Huang, Houkuan Ye, Dongyi Hu, Qinghua Hu, Xuegang Shi, Zhongzhi Li, Yongli Shang, Lin An, Liping Sai, Ying Chen, Shanben Liang, Jiye Qin, keyun Zeng, Huanglin Xie, Keming Miao, Duoqian Min, Fan Wu, Zhaocong Wu, Weizhi Dai, Jianhua Institute of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing 400065 China School of Information Science and Technology Southwest Jiaotong University Chengdu 610031 China School of Computer and Information Technology Beijing Jiaotong University Beijing 100044 China College of Math. and Computer Fuzhou University Fuzhou 350002 China Harbin Institute of Technology PO Box 458 Harbin 150006 China School of Computer and Information Hefei University of Technology Hefei 230009 China Institute of Computing Technology Chinese Academy of Sciences Beijing 100080 China Lanzhou University No.222 Tianshui South Rd. Lanzhou 730000 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing 210093 China Management Science and Engineering Department Business School Nankai University Tianjin 300071 China Shandong University of Finance No.40 Shun-geng Road Jinan 250014 China Welding Engineering Institute Shanghai Jiaotong University Shanghai 200030 China School of Computer and Information Technology Shanxi University Taiyuan 030006 China Intelligent Control Development Center Southwest Jiaotong University Chengdu 610031 China Artificial Intelligence of Key Laboratory Sichuan University of Science and Engineering Zigong 643000 China Department of Automation College of Information Engineering Taiyuan University of Technology Taiyuan 030024 China 4800 CaoAn Highway Shanghai 201804 China School of Computer Science and Technology University of Electronic Science and Technology of China Chengdu 610054 China School of Remote Sensing Information Engineering Wuhan University Wuhan 430079 China Institute for Information and System Sciences Faculty of Science Xi'an Jiaotong University Xi'an 710049 China Information College Zhejiang Ocean University Zhejiang 316004 China Institute of Artificial Intelligence Zhejiang University Hangzhou 310027 China

ISBN: (纸本)3540850635

This article gives a capsule view of research on rough set theory and applications ongoing at universities and laboratories in China. Included in this capsule view of rough set research is a brief description of the following things: Chinese research groups on rough set with their URLs for web pages, names of principal researchers (supervisors), numbers of graduate students, and topics being investigated. Statistical summaries showing the growth in the research on rough set theory and application in China are included. In addition, an introduction summarizing the research interests of Chinese researchers is included in this article. The contribution of this article is a complete overview of the principal research directions in rough set theory and its applications in China. © 2008 Springer-Verlag Berlin Heidelberg.

关键词： Rough set theory

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Well limit behaviors of term rewriting systems

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中国高等学校学术文摘·计算机科学 2007年第3期1卷 283-296页

作者： MA Shilong SUI Yuefei XU Ke State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100083China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of SciencesBeijing 100080China

The limit behaviors of computations have not been fully *** is necessary to consider such limit behaviors when we consider the properties of infinite objects in computer science,such as infinite logic programs,the symbolic solutions of infinite polynomial ***,we can use finite objects to approximate infinite objects,and we should know what kinds of infinite objects are approximable and how to approximate them effectively.A sequence {Rκ:κω}of term rewriting systems has the well limit behavior if under the condition that the sequence has the Set-theoretic limit or the distance-based limit,the sequence {Th(Rκ):κ∈ω} of corresponding theoretic closures of Rκ has the set-theoretic or distance-based limit,and limκ→∞ Th(Rκ) is equal to the theoretic closure of the limit of {Rκ:κ∈ω).Two kinds of limits of term rewriting systems are considered:one is based on the set-theoretic limit,the other is on the distance-based *** is proved thatgiven a sequence {Rκ:κ∈ω) of term rewriting systems Rκ,if there is a well-founded ordering (-＜) on terms such that every Rκ is (-＜)-well-founded,and the set-theoretic limit of {Rκ:κ∈ω).exists,then {Rκ:κ∈ω).has the well limit behavior;and if (1) there is a well-founded ordering(-＜)on terms such that every Rκ is(-＜-well-founded,(2) there is a distance d on terms which is closed under substitutions and contexts and (3) {Rκ:κ∈ω).is Cauchy under d then {Rκ:κ∈ω).has the well limit *** results are used to approximate the least Herbrand models of infinite Horn logic programs and real Horn logic programs,and the solutions and Cr(o)bner bases of (infinite) sets of real polynomials by sequences of (finite) sets of rational polynomials.

关键词： term rewriting systems limits Gr(o)bner bases well-foundedness

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Enhancing Training Set for Face Detection Based on SVM

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Ruan Jian Xue Bao/Journal of Software 2008年第11期19卷 2921-2931页

作者： Wang, Rui-Ping Chen, Jie Shan, Shi-Guang Chen, Xi-Lin Gao, Wen Key Laboratory of Intelligent Information Processing The Chinese Academy of Sciences Beijing 100190 China Institute of Computing Technology The Chinese Academy of Sciences Beijing 100190 China Graduate University The Chinese Academy of Sciences Beijing 100049 China School of Computer Science and Technology Harbin Institute of Technology Harbin 150001 China School of Electronics Engineering and Computer Science Peking University Beijing 100871 China

According to support vector machines (SVMs), for those geometric approach based classification methods, examples close to the class boundary usually are more informative than others. Taking face detection as an example, this paper addresses the problem of enhancing given training set and presents a nonlinear method to tackle the problem effectively. Based on SVM and improved reduced set algorithm (IRS), the method generates new examples lying close to the face/non-face class boundary to enlarge the original dataset and hence improve its sample distribution. The new IRS algorithm has greatly improved the approximation performance of the original reduced set (RS) method by embedding a new distance metric called image Euclidean distance (IMED) into the kernel function. To verify the generalization capability of the proposed method, the enhanced dataset is used to train an AdaBoost-based face detector and test it on the MIT+CMU frontal face test set. The experimental results show that the original collected database can be enhanced effectively by the proposed method to learn a face detector with improved generalization performance.

关键词： Support vector machines

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