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International Conference on Computational Intelligence and Security

作者： Yutao Qi Fang Liu Licheng Jiao School of Computer Science And Technology Key Laboratory of Intelligent Perception and Imagee Understanding of Ministry of Education Xidian University Xi'an China Institute of Intelligent Information Processing and Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Xi'an China

This paper presents a parallel artificial immune model termed as tower master-slave model (TMSM) for solving optimising problems. Based on TMSM, the parallel immune memory clonal selection algorithm (PIMCSA) is also proposed. TMSM is a two level coarse-grained parallel artificial immune model with distributed immune response and distributed immune memory. In PIMCSA, vaccines are extracted and migrated between populations rather than individual migration as has been done in parallel genetic algorithms. It is a good balance between population diversity and the convergent speed. Experimental results on the numerical optimization and TSP problems show that PIMCSA achieves good performance in terms of both solution quality and computation time.

关键词： Immune system Genetic algorithms Artificial immune systems Competitive intelligence Master-slave Laboratories Computer science education Poles and towers Vaccines Parallel processing

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Single vs. population cell coding: Gaze movement control in target search

Single vs. population cell coding: Gaze movement control in ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Jun Miao Laiyun Qing Lijuan Duan Baixian Zou Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China School of Information Science and Engineering Chinese Academy of Sciences Beijing China College of Computer Science and Technology University of Technology Beijing China Department of Information Science and Technology College of Art and Science Beijing Union Technology Beijing China

Gaze movement plays an important role in human visual search system. In literature, the winner-take-all method is wildly used to simulate the controlling of the gaze movement. The winner-take-all is a type of single-cell coding method, which uses one cell (grandmother cell) or one response to represent an object. However, eye movement is affected by the visual context which includes more than one object in images, especially in target search. Therefore, we propose to use the population coding with more than one response rather than the single-cell coding on gaze movement control. The proposed method is supported by the theoretical analysis and experiments on a real image database which show the population-cell-coding improves the target locating accuracy by 44.4% only at the cost of coding 22.4% more information than that of single-cell-coding.

关键词： Humans Image coding Image databases Costs information science Educational institutions Biological neural networks Control systems Image analysis information analysis

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Research on the method of entity homepage recognition

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Journal of Computational information Systems 2009年第6期5卷 1617-1624页

作者： Li, Lina Yu, Zhengtao Zou, Junjie Su, Lei Xian, Yantuan Mao, Cunli The School of Information Engineering and Automation Kunming University of Science and Technology Kunming 650051 China The Institute of Intelligent Information Processing Computer Technology Application Key Laboratory of Yunnan Province Kunming 650051 China School of Software Yunnan University Kunming 650091 China

Entity Homepage Recognition(EHR) is an important part of entity finding. In this paper, a method of EHR based on AdaBoost is proposed. With the help of Google, this method recalls pages related to answer entities and recognizes to obtain the relevant entity homepage. In detail, Person, Organization and Product, three types of entities are analyzed respectively and 3148 entities and the corresponding entity homepages are collected, besides, on the basis of entity characteristic, we define the entity features related to the features of link and webpage content, as well as, these features are extracted to form a training data set;And then using different learning algorithms with different features to identify the homepage to find the most effective classification features and homepage recognition learning algorithm. Experiments were done by employing the test data set of entity task in TREC-2009 and the method of 10-fold cross validation, the results show the best effects have been achieved by adopting the method of AdaBoost, specifically, combined with the features of link and webpage content, the recognition accuracy rate for right homepage reaches 91.34%. 1553-9105/ Copyright © 2009 Binary information Press.

关键词： Adaptive boosting

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Granularity-tunable gradients partition (GGP) descriptors for human detection

Granularity-tunable gradients partition (GGP) descriptors fo...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yazhou Liu Shiguang Shan Wenchao Zhang Xilin Chen Wen Gao School of Computer Science and Technology Harbin Institute of Technology Harbin China Key Laboratory of Intelligent Information Processing Chinese Academy and Sciences Beijing China Institute of Digital Media Peking University Beijing China

This paper proposes a novel descriptor, granularity-tunable gradients partition (GGP), for human detection. The concept granularity is used to define the spatial and angular uncertainty of the line segments in the Hough space. Then this uncertainty is backprojected into the image space by orientation-space partitioning to achieve efficient implementation. By changing the granularity parameter, the level of uncertainty can be controlled quantitatively. Therefore a family of descriptors with versatile representation property can be generated. Specifically, the finely granular GGP descriptors can represent the specific geometry information of the object (the same as Edgelet); while the coarsely granular GGP descriptors can provide the statistical representation of the object (the same as histograms of oriented gradients, HOG). Moreover, the position, orientation, strength and distribution of the gradients are embedded into a unified descriptor to further improve the GGP's representation power. A cascade structured classifier is built by boosting the linear regression functions. Experimental results on INRIA dataset show that the proposed method achieves comparable results to those of the state-of-the-art methods.

关键词： Humans Shape Uncertainty Histograms Support vector machines Support vector machine classification Boosting Detectors Covariance matrix Noise robustness

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Boid particle swarm optimisation

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International Journal of Innovative computing and Applications 2009年第2期2卷 78-85页

作者： Cui, Zhihua Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences No. 6 Kexueyuan South Rd. Zhongguancun Haidian District Beijing 100190 China Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology No. 66 Waliu Rd. Wanbailin District Taiyuan Shanxi 030024 China

Particle swarm optimisation (PSO) is a novel population-based stochastic optimisation algorithm inspired by the Reynolds' boid model. The original biological background of boid obeys three basic simple steering rules: separation, alignment and cohesion. However, to promote a simple update equation, none of these rules of boid model is employed by PSO methodology. Due to the weakness of biological background of PSO, in this paper, a new variant of PSO, boid particle swarm optimisation (BPSO), is designed in which cohesion rule and alignment rule are both employed to improve the performance. In BPSO, each particle has two motions: divergent motion and convergent motion. For divergent motion, each particle adjusts its moving direction according to the alignment direction and the cohesion direction, as well as in convergent motion, the original update equation of the standard version of PSO is used. To make a motion transition, a threshold is introduced to make the divergent motion is employed in the first period, whereas the convergent motion is used in the final stage. To testify the efficiency, several unconstrained benchmarks are used to compare. Simulation results show the proposed variant is more effective and efficient than other two variants of PSO when solving multi-modal high-dimensional numerical problems. Copyright © 2009 Inderscience Enterprises Ltd.

关键词： Alignment

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Linear support vector machine based on kernel function

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Journal of Computational information Systems 2009年第4期5卷 1089-1095页

作者： Gao, Shang Hu, Xuekun Zhang, Zaiyue Cao, Cungen School of Computer Science and Engineering Jiangsu University of Science and Technology Zhenjiang 212003 China Provincial Key Laboratory of Computer Information Processing Technology Soochow University Suzhou 215006 China Institute of Computing Technology The Chinese Academy of Sciences Beijing 100080 China

Support vector machine is kind of novel machine learning method, based on statistical learning theory, which becomes the hotspot of machine learning because of its excellent learning performance. The method of support vector machine has been developed for solving classification and regression problems. Training a support vector machine requires the solutions of a very large quadratic programming problem. The algorithm is usually slow when there are many data. In order to improve the running speed, three regression models based on kernel function are introduced. One of three models is changed into a system of linear equations problem, the other models are changed into linear programming problems and their dual problems are given. Simulation results show the effectiveness of the methods, and the computation complexity are largely reduced. The kernel function and the parameter of models selection are compared and analyzed by means of an example. © 2009 Binary information Press August, 2009.

关键词： Support vector machines

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Active Learning for Chinese Word Sense Disambiguation

Active Learning for Chinese Word Sense Disambiguation

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International Conference on Innovative computing, information and Control (ICICIC)

作者： Peng Jin Yunfang Wu Zuxu Dai Yi Liu Laboratory of Intelligent Information Processing and Application Leshan Normal University Leshan China Key Laboratory of Computational Linguistics Ministry of Education Peking University China College of Science Wuhan Institute of Technology Wuhan China

Word sense disambiguation (WSD) suffers from the lack of large scale corpus which annotated with word senses. The reason is that creating this corpus with human annotation is time-consuming and expensive. Active learning could save the human effort to obtain the same quality corpus. We investigate eight Chinese polysemous words and show the effectiveness of active learning for Chinese WSD.

关键词： Entropy Training data Uncertainty Laboratories Sampling methods information processing Large-scale systems Humans Testing Equations

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Fast car detection using image strip features

Fast car detection using image strip features

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Wei Zheng Luhong Liang Graduate School of Chinese Academy of Sciences Beijing China Institute of Computing Technology Chinese Academy and Sciences Beijing China Key Lab of Intelligent Information Processing Chinese Academy of Sciences (CAS) Beijing 100190 China

This paper presents a fast method for detecting multi-view cars in real-world scenes. Cars are artificial objects with various appearance changes, but they have relatively consistent characteristics in structure that consist of some basic local elements. Inspired by this, we propose a novel set of image strip features to describe the appearances of those elements. The new features represent various types of lines and arcs with edge-like and ridge-like strip patterns, which significantly enrich the simple features such as haar-like features and edgelet features. They can also be calculated efficiently using the integral image. Moreover, we develop a new complexity-aware criterion for RealBoost algorithm to balance the discriminative capability and efficiency of the selected features. The experimental results on widely used single view and multi-view car datasets show that our approach is fast and has good performance.

关键词： Strips Face detection Object detection Biological system modeling Deformable models Computer vision Content addressable storage Support vector machines Humans Wheels

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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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A ROI quality adjustable rate control scheme for low bitrate video coding

A ROI quality adjustable rate control scheme for low bitrate...

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Picture Coding Symposium, PCS

作者： Ling Yang Li Zhang Siwei Ma Debin Zhao Chinese Academy of Sciences Beijing China Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China Institute of Digital Media School of Electronic Engineering and Computer Science Peking University Beijing China Department of Computer Science and Engineering Harbin Institute of Technology Harbin China

This paper proposed a novel Region of Interest (ROI) based rate control algorithm for video communication systems, in which the subjective quality of ROI can be adjusted according to users' requirements. In the proposed scheme, in order to analyze the relationship between subjective quality and encoding parameters, a structural similarity index map - quantization parameter (SSIM-QP) model is established. Through this relation, the possible visual quality range of ROI is defined according to the range of ROI QP, which is predicted by rate control algorithm. Then, with interest levels being identified within the visual quality range, resources allocated to ROI is determined. Finally, considering both the quality of ROI and the entire frame, resource allocation is slightly adjusted. Experimental results demonstrate that the subjective visual quality of ROI in our proposed scheme can be adjustable improved compared to the existing rate control algorithms.

关键词： Bit rate Video coding Quadratic programming Humans Computer science Quantization Resource management Videoconference PSNR Data mining

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