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International Conference on Pattern Recognition

作者： Sulan Zhang Chunqi Shi Zhiyong Zhang Zhongzhi Shi Chinese Academy of Sciences China Institute of Computing Technology Key Laboratory of Intelligent Information Processing Chinese Academy and Sciences China

ISBN: (纸本)0769525210

We propose an appearance-based image clustering approach called GGCI (global geometric clustering for image). For face images taken with varying pose, expression, eyes (wearing sunglasses or not) or object images under different viewing conditions, GGCI uses easily measured local metric information to learn the underlying global geometry of images space, then apply the extended nearest neighbor approach to cluster images. Different from the usual nearest neighbor approach, GGCI considers the density around the nearest points within clusters. Moreover, our approach clusters based on the geodesic distance measure instead of Euclidean distance measure, which better reflects the intrinsic geometric structure of manifold embedded in high dimensional image space. Experimental results suggest that the proposed GGCI approach achieves lower error rates in image clustering when manifolds are embedded in image space

关键词： Level measurement Nearest neighbor searches Feature extraction Geophysics computing Eyes Euclidean distance Photoreceptors information processing Computers information geometry

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Mean-shift blob tracking with adaptive feature selection and scale adaptation

Mean-shift blob tracking with adaptive feature selection and...

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14th IEEE International Conference on Image Processing, ICIP 2007

作者： Dawei, Liang Qingming, Huang Shuqiang, Jiang Hongxun, Yao Wen, Gao School of Computer Science and Technology Harbin Institute of Technology Harbin 150001 China Graduate School Chinese Academy of Sciences Beijing 100080 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100080 China Institute of Digital Media Peking University Beijing 100871 China

ISBN: (纸本)1424414377

When the appearances of the tracked object and surrounding background change during tracking, fixed feature space tends to cause tracking failure. To address this problem, we propose a method to embed adaptive feature selection into mean shift tracking framework. From a feature set, the most discriminative features are selected after ranking these features based on their Bayes error rates, which are estimated from object and background samples. For the selected features, a criterion is proposed to evaluate their stability for tracking and to guide feature reselection. The selected features are used to generate a weight image, in which mean shift is employed to locate the object. Moreover, a simple yet effective scale adaptation method is proposed to deal with object changing in size. Experiments on several video sequences show the effectiveness of the proposed method. ©2007 IEEE.

关键词： Feature Selection

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Completeness of Query Operations on Resource Spaces

Completeness of Query Operations on Resource Spaces

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International Conference on Semantics, Knowledge and Grid (SKG)

作者： Hai Zhuge Hai Zhuge Erlin Yao Erlin Yao China Knowledge Grid Research Group Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

A great variety of languages can be designed by different people for different purposes to operate resource spaces. Two fundamental issues are: can we design more operations in addition to existing operations? and, how many operations are sufficient or necessary? This paper solves these problems by investigating the theoretical basis for determining how complete a selection capability is provided in a resource operation sublanguage independent of any host language. The result is very useful to the design and analysis of operating languages.

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A computing Model for Concept Fusing and Document Classification

A Computing Model for Concept Fusing and Document Classifica...

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International Conference on Semantics, Knowledge and Grid (SKG)

作者： Nan Zhang Nan Zhang Chao He Chao He China Knowledge Grid Research Group Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

Effective document classification is a long-pursued goal in knowledge management. This paper proposes a novel hybrid approach of semantic representation and statistical measurements. Document is divided into content segments first. By Formal Concept Analysis (FCA), their semantic links with standard concept identifiers are built up whose weights are calculated statistically. In this way, effective concept fusing and document classification can be achieved. In addition, a semantic overlay for specific documents will be constructed via concept fusing. Experiments show our approach is feasible and effective.

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Period information detection from motion capture data

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Journal of Computational information Systems 2006年第2期2卷 551-557页

作者： Li, Zhenbo Deng, Yu Li, Hua Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100080 China Graduate School Chinese Academy of Sciences Beijing 100039 China

This paper introduces an approach to detect period information from motion capture data in low dimension. After a motion capture data is obtained, PCA method is utilized for dimension reduction. The accumulation contribution factor is used to determine the analysis dimension. Then the algorithm outputs the period through the low dimension computation. The method is assessed on CMU motion capture database and compared the performance of the automatic methods to that of manually selection. The results show that it has good characteristic for both simple and complex motions. This method can be used for motion database management and motion synthesization.

关键词： information technology

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Classification Algorithm of Land Quality Based on PLS

Classification Algorithm of Land Quality Based on PLS

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World Congress on intelligent Control and Automation (WCICA)

作者： Shifei Ding Zhongzhi Shi Yong Liang College of Information Science and Engineering Shandong Agricultural University Taian China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China

Based on modeling idea of partial least squares (PLS) and divided the values of response variable into two classes denoted by 0 and 1, a novel classification algorithm of land quality is set up in this paper. Firstly, the algorithms of multiple linear regression (MLR) and principal component regression (PCR) are introduced and analysed their shortages. Then on the basis of modeling idea of PLS, the classification algorithm of land quality is constructed. The experiment shows that the PLS algorithm doesn't request distribution of the data, and has best classification pattern ability compared with the algorithms of MLR and PCR. It has more advantages than MLR, PCR, such as simplicity and robustness, clearly qualitative explanation. It is powerful for multicollinearity, particularly when the number of predictor variables is large and the sample size is small, and provides a novel research method for classification of land quality

关键词： Classification algorithms Least squares methods Electronic mail Linear regression Principal component analysis Educational institutions information science Agricultural engineering Laboratories information processing

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Online Infomax Algorithm and its Application to Remove EEG Artifacts

Online Infomax Algorithm and its Application to Remove EEG A...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Xiaopei Wu Zhongfu Ye School of Information Science and Technology University of Science and Technology Hefei Anhui China The Key Laboratory of Intelligent Computing & Signal Processing Anhui University Hefei Anhui China

An online infomax algorithm is proposed in this paper. The performances and properties of this online algorithm is investigated in detail. To the problem of the artifacts removal in real life EEG signal, both the online-and batch infomax algorithm are applied and compared. The experiment results show that the online infomax algorithm proposed in this paper has the good performance both in artifacts removal and convergence in time-varying mixing system

关键词： Electroencephalography Signal processing algorithms Independent component analysis Equations Electrocardiography Gaussian processes World Wide Web Biology computing Biomedical engineering Biomedical signal processing

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Classification algorithm of land quality based on PLS

Classification algorithm of land quality based on PLS

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6th World Congress on intelligent Control and Automation, WCICA 2006

作者： Shifei, Ding Zhongzhi, Shi Yong, Liang College of Information Science and Engineering Shandong Agricultural University Taian 271018 Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100080

ISBN: (纸本)1424403324

Based on modeling idea of partial least squares (PLS) and divided the values of response variable into two classes denoted by 0 and 1, a novel classification algorithm of land quality is set up in this paper. Firstly, the algorithms of multiple linear regression (MLR) and principal component regression (PCR) are introduced and analysed their shortages. Then on the basis of modeling idea of PLS, the classification algorithm of land quality is constructed. The experiment shows that the PLS algorithm doesn't request distribution of the data, and has best classification pattern ability compared with the algorithms of MLR and PCR It has more advantages than MLR, PCR, such as simplicity and robustness, clearly qualitative explanation. It is powerful for multicollinearity, particularly when the number of predictor variables is large and the sample size is small, and provides a novel research method for classification of land quality. © 2006 IEEE.

关键词： Image quality

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Context Optimization of AI planning for Services Composition

Context Optimization of AI planning for Services Composition

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IEEE International Conference on e-Business Engineering (ICEBE)

作者： Lirong Qiu Zhongzhi Shi Fen Lin Graduate School of Chinese Academy of Sciences Beijing China Institute of Computing Technology Key Laboratory of Intelligent Information Processing Chinese Academy and Sciences Beijing China

Web services composition techniques are gaining momentum as the opportunity to establish reusable and versatile inter-operability applications. Many researchers propose their composition approach based on planning techniques. We propose our context aware planning method which comprises global planning and local optimization based on context information. The major technical contributions of this paper are: (1) we propose an ontology-based framework for the context-aware composition of Web services. Context model, which are structured based on OWL-S, captures the service-related, environment-related, and user-related context and can be used in an unambiguous, machine interpretable form. (2) We propose context-aware plan architecture and thus is more scalability and flexibility for the planning process, and thereby improving the efficiency and precision. (3) We propose a hybrid approach to build a plan corresponding to a context-aware service composition, based on global planning and local optimization, considering both the usability and adoption. We test our approach on a simple, yet realistic example, and the preliminary results demonstrate that our implementation provides a practical solution

关键词： Context-aware services Artificial intelligence Web services Context awareness Process planning Optimization methods Ontologies Context modeling Scalability Usability

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3D gait recognition using multiple cameras

3D gait recognition using multiple cameras

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International Conference on Automatic Face and Gesture Recognition

作者： Guoying Zhao Guoyi Liu Hua Li M. Pietikainen Machine Vision Group Infotech Oulu and Department of Electrical and Information Engineering University of Oulu Finland Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China

Gait recognition is used to identify individuals in image sequences by the way they walk. Nearly all of the approaches proposed for gait recognition are 2D methods based on analyzing image sequences captured by a single camera. In this paper, video sequences captured by multiple cameras are used as input, and then a human 3D model is set up. The motion is tracked by applying a local optimization algorithm. The lengths of key segments are extracted as static parameters, and the motion trajectories of lower limbs are used as dynamic features. Finally, linear time normalization is exploited for matching and recognition. The proposed method based on 3D tracking and recognition is robust to the changes of viewpoints. Moreover, better results are achieved for sequences containing difficult surface variations than with 2D methods, which prove the efficiency of our algorithm

关键词： Cameras Image recognition Image sequences Image analysis Image sequence analysis Video sequences Humans Tracking Image segmentation Robustness

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