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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Xiaopeng Hong Hong Chang Shiguang Shan Xilin Chen Wen Gao School of Computer Science and Technology Harbin Institute of Technology China Institute of Computing Technology Chinese Academy and Sciences China Key Laboratory of Intelligent Information Processing Chinese Academy and Sciences China Institute of Computing Technology Chinese Academy of Sciences China Institute for Digital Media Peking University China

Given an image region of pixels, second order statistics can be used to construct a descriptor for object representation. One example is the covariance matrix descriptor, which shows high discriminative power and good robustness in many computer vision applications. However, operations for the covariance matrix on Riemannian manifolds are usually computationally demanding. This paper proposes a novel second order statistics based region descriptor, named “Sigma Set”, in the form of a small set of vectors, which can be uniquely constructed through Cholesky decomposition on the covariance matrix. Sigma Set is of low dimension, powerful and robust. Moreover, compared with the covariance matrix, Sigma Set is not only more efficient in distance evaluation and average calculation, but also easier to be enriched with first order statistics. Experimental results in texture classification and object tracking verify the effectiveness and efficiency of this novel object descriptor.

关键词： Robustness Histograms Fractals Lighting Statistics Solids computer science Power engineering and energy Mathematics Power engineering computing

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Several progress of semi-supervised learning

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Journal of information and Computational Science 2009年第1期6卷 211-217页

作者： Ding, Shifei Su, Chunyang Jia, Weikuan Jin, Fengxiang Shi, Zhongzhi School of Computer Science and Technology China University of Mining and Technology Xuzhou 221008 China Key Laboratory of Intelligent Information Processing Chinese Academy of Sciences Beijing 100080 China College of Plant Protection Shandong Agricultural University Taian 271018 China College of Geoinformation Science and Engineering Shandong University of Science and Technology Qingdao 266510 China

In this paper we discuss several progress of semi-supervised learning, making emphasis on semi-supervised classification. First, we introduce the history and basic concept and methods of semi-supervised learning, then explain the algorithms of semi-supervised learning in details, and discuss the current situation of semi-supervised learning, at last we look forward to the development trend of semi-supervised learning. © 2009 Binary information Press.

关键词： Supervised learning

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Novel contour-feature retrieval algorithm

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Guangdianzi Jiguang/Journal of Optoelectronics Laser 2009年第1期20卷 108-112页

作者： Sun, Jun-Ding Li, Quan-Xi Wu, Xiao-Sheng School of Computer Science and Technology Henan Polytechnic University Jiaozuo 454003 China Provincial Key Laboratory of Computer Information Processing Technology Suzhou University Suzhou 215006 China

The paper presents a novel approach for contour-based shape retrieval. The contour is firstly sampled and smoothed by a Gaussian filter. Then, the sampled points are classified into salient (convex or concave) or smooth type points by their internal angles. In addition to distance histograms (DH), two new descriptors named relative address distribution (RAD) and relative unit entropy (RUE) are introduced to describe the correlation of each type of contour points. These descriptors have powerful descriptive ability for contour with more spatial information. Comparisons are conducted between the proposed method and several other feature descriptors. The results show that the new method is efficient and it provides noticeable improvement to the performance of shape retrieval.

关键词： Entropy

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Recent development of the HMM-based speech synthesis system (HTS)

Recent development of the HMM-based speech synthesis system ...

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Asia-Pacific Signal and information processing Association 2009 Annual Summit and Conference, APSIPA ASC 2009

作者： Zen, Heiga Oura, Keiichiro Nose, Takashi Yamagishi, Junichi Sako, Shinji Toda, Tomoki Masuko, Takashi Black, Alan W. Tokuda, Keiichi Department of Computer Science Nagoya Institute of Technology Nagoya 466-8555 Japan Department of Information Processing Interdisciplinary Tokyo Institute of Technology Yokohama 226-8502 Japan Centre for Speech Technology Research University of Edinburgh Edinburgh EH8-9LW United Kingdom Graduate School of Information Science Nara Institute of Science and Technology Ikoma 630-0192 Japan Language Technologies Institute Carnegie Mellon University Pittsburgh PA 15213 United States Cambridge Research Laboratory Toshiba Research Europe Limited Cambridge CB4 0GZ United Kingdom Corporate Research and Development Center Toshiba Corporation Kawasaki 212-8582 Japan

A statistical parametric approach to speech synthesis based HMMs has grown in popularity over the last few years. In this approach, spectrum, excitation, and duration of speech are simultaneously modeled by context-dependent HMMs, and speech waveforms are generated from the HMMs themselves. Since December 2002, we have publicly released an opensource software toolkit named "HMM-based speech synthesis system (HTS)" to provide a research and development toolkit for statistical parametric speech synthesis. This paper describes recent developments of HTS in detail, as well as future release plans.

关键词： Speech synthesis

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A Novel Hebbian Rules Based Method for Computation of Sparse Coding Basis Vectors

A Novel Hebbian Rules Based Method for Computation of Sparse...

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International Conference on Natural Computation (ICNC)

作者： Baixian Zou Jun Miao Xiaoling Yang Lijuan Duan Yuanhua Qiao Department of Information Science and Technology College of Arts and Science of Beijing Union University Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China Tianjin Academy of Agricultural Science Tianjin China College of Computer Science and Technology Beijing Beijing China College of Applied Science Beijing University of Technology Beijing China

Sparse coding has high-performance encoding and ability to express images, sparse encoding basis vector plays a crucial role. The computational complexity of the most existing sparse coding basis vectors of is relatively large. In order to reduce the computational complexity and save the time to train basis vectors. A new Hebbian rules based method for computation of sparse coding basis vectors is proposed in this paper. A two-layer neural network is constructed to implement the task. The main idea of our work is to learn basis vectors by removing the redundancy of all initial vectors using Hebbian rules. The experiments on natural images prove that the proposed method is effective for sparse coding basis learning. It has the smaller computational complexity compared with the previous work.

关键词： computer science Stability Space technology Collaboration Mathematical model Euclidean distance Extraterrestrial measurements Particle measurements Fitting Game theory

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Editorial: Game theory in signal processing and communications

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Eurasip Journal on Advances in Signal processing 2009年 2009卷

作者： Jorswieck, Eduard A. Boche, Holger Han, Zhu Larsson, Erik G. Department of Mobile Communications School of Electrical Engineering and Computer Sciences Technical University of Berlin Berlin Germany Wireless Networking Signal Processing and Security Lab Department of Electrical and Computer Engineering University of Houston Houston TX 77004 United States Linkping University SE-581 83 Linkping Sweden Communications Laboratory Faculty of Electrical Engineering and Information Technology Dresden University of Technology 01062 Dresden Germany

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Eye movement data modeling using a genetic algorithm

Eye movement data modeling using a genetic algorithm

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Congress on Evolutionary Computation

作者： Yun Zhang Hong Fu Zhen Liang Xiaoyu Zhao Zheru Chi Dagan Feng Xinbo Zhao School of Computer Science Northwestern University Evanston IL USA Center of Multimedia Signal Processing Hong Kong Polytechnic University Hong Kong China School of Information Technologies University of Sydney NSW Australia Key Laboratory of Contemporary Design & Integrated Manufacturing Technology Northwestern Polytechnical University Xi'an Shanxi China

We present a computational model of human eye movements based on a genetic algorithm (GA). The model can generate elemental raw eye movement data in a four-second eye viewing window with a 25 Hz sampling rate. Based on the physiology and psychology characters of human vision system, the fitness function of the GA model is constructed by taking into consideration of five factors including the saliency map, short time memory, saccades distribution, Region of Interest (ROI) map, and a retina model. Our model can produce the scan path of a subject viewing an image, not just several fixations points or artificial ROI's as in the other models. We have also developed both subjective and objective methods to evaluate the model by comparing its behavior with the real eye movement data collected from an eye tracker. Tested on 18 (9 times 2) images from both an obvious-object image group and a non-obvious-object image group, the subjective evaluations shows very close scores between the scan paths generated by the GA model and those real scan paths; for the objective evaluation, experimental results show that the distance between GA's scan paths and human scan paths of the same image has no significant difference by a probability of 78.9% on average.

关键词： Genetic algorithms Humans Computational modeling Sampling methods Physiology Psychology Machine vision Retina Tracking Testing

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A parallel optimization model for massive data stream application

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Ruan Jian Xue Bao/Journal of Software 2009年第SUPPL. 1期20卷 23-33页

作者： Sun, Xiao-Juan Sun, Ning-Hui Lei, Bin Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Institute of Electronics The Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Computer System and Architecture Institute of Computing Technology The Chinese Academy of Sciences Beijing 100190 China

While computing is entering a new phase in which CPU improvements are driven by the addition of multiple cores on a single chip, rather than higher frequencies. Parallel processing on these systems is in a primitive stage, and requires the explicit use and knowledge of underlying thread architecture. Based on the features of massive data stream application, this paper proposes three-level pipelining programming model of multithreading system, which realizes the new synchronization mechanism with no contention of shared structures and is capable to provide differential service for data streams. Then the paper applies the new model to remote sensing information processing system and backbone network intrusion detection system, and evaluates the improved system on several multicore platforms. In performance analysis, the optimized effects of backbone network intrusion detection system are evaluated in several aspects of throughput scalability on both SPARC T1 and x86 platforms, the impacts of different multithreading mapping methods on throughput, and the comparison of response time and service quality before and after optimization. The experimental results show that the system throughput has good scalability on both platforms, the values of response time are greatly improved and the prioritized streams achieve better response time with the differential service mechanism. © by Institute of Software, the Chinese Academy of Sciences. All rights reserved.

关键词： Metadata

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Data-deficiency-tolerated method for viewpoint independent sign language recognition

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Jisuanji Xuebao/Chinese Journal of computers 2009年第5期32卷 953-961页

作者： Wang, Qi Chen, Xi-Lin Wang, Chun-Li Gao, Wen School of Computer Science and Technology Harbin Institute of Technology Harbin 150001 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China School of Information Science and Technology Dalian Maritime University Dalian 116026 China School of Electronics Engineering and Computer Science Peking University Beijing 100871 China

This paper proposes a Sample-Consensus method for viewpoint independent sign language recognition under data deficiency (matched features are possibly deficient with regard to some frame pairs). The proposed method is based on the epipolar geometry and inspired by RANSAC. The basic idea is that all corresponded frames between two sequences of the same sign can be roughly considered as captured synchronously by a virtual stereo vision system and thus they will satisfy the same fundamental matrix. In addition, the fundamental matrix can be estimated from point correspondences contained by some part of corresponding frames. Experimental results demonstrate the efficiency of the proposed method. Moreover, this Sample-Consensus method can be easily extended to some similar problems, such as viewpoint independent activity analysis and rigid-motion analysis.

关键词： Stereo image processing

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Multivariate classification of deep web sources based on maximum entropy model

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Journal of Computational information Systems 2008年第3期4卷 1093-1100页

作者： Huang, Li Zhao, Pengpeng Cui, Zhiming Wei, Fang Institute of Intelligent Information Processing and Application Soochow University Suzhou 215006 China Jiangsu Key Laboratory of Computer Information Processing Technology Soochow University Suzhou 215006 China

With the fast development of World Wide Web, the quantity of web information is increasing in an unprecedented pace, a great many of which are generated dynamically from background databases, and can't be indexed by traditional search engine, so we call them Deep Web. For the heterogeneous and dynamic features of Deep Web sources, classifying the Deep Web source by domain effectively is a significant precondition of Deep Web sources integration. In this paper, we consider the visible features of Deep Web and Maximum Entropy approach, and then on the basis of binary classification, we propose a new multivariate classification approach based on Maximum Entropy towards Deep Web sources. In addition, we propose a Feedback algorithm to improve the accuracy of classification. An experimental evaluation over real Web data shows that, our approach could provide an effective and general solution to the multivariate classification of Deep Web sources.

关键词： Search engines

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