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International Symposium on Ubiquitous Virtual Reality, ISUVR

作者： Fei Ye Zhiping Shi Zhongzhi Shi Chinese Academy of Sciences Beijing Beijing CN Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China

Although various discriminant analysis approaches have been used in content-based image retrieval (CBIR) application, there have been relatively few concerns with kernel-based methods. Furthermore, these CBIR applications still applied discriminant analysis to face images as face recognition did. In this paper we concerns images with general semantic concepts. We use our presented symmetrical invariant LBP (SILBP) texture descriptor to extract image visual features. We then explored effectiveness of principal component analysis (PCA), fisher linear discriminant analysis (LDA), and kernel LDA algorithms in providing optimal discrimination features. Following it, we present an LDA based framework to carry out kernel discrimiant analysis in our application. By taking advantage of the efficiency in nonlinear condition of kernel-based methods and the simplicity of LDA, the proposed approach can improve the retrieval precision of CBIR. The experimental results validate the effectiveness of the proposed approach.

关键词： Linear discriminant analysis Principal component analysis Kernel Image classification Image analysis Information retrieval Image retrieval Content based retrieval Face recognition Feature extraction

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Adaptive action selection using utility-based reinforcement learning

Adaptive action selection using utility-based reinforcement ...

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IEEE International Conference on Granular computing (GRC)

作者： Kunrong Chen Fen Lin Qing Tan Zhongzhi Shi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China Chinese Academy of Sciences Beijing Beijing CN

A basic problem of intelligent systems is choosing adaptive action to perform in a non-stationary environment. Due to the combinatorial complexity of actions, agent cannot possibly consider every option available to it at every instant in time. It needs to find good policies that dictate optimum actions to perform in each situation. This paper proposes an algorithm, called UQ-learning, to better solve action selection problem by using reinforcement learning and utility function. Reinforcement learning can provide the information of environment and utility function is used to balance exploration-exploitation dilemma. We implement our method with maze navigation tasks in a non-stationary environment. The results of simulated experiments show that utility-based reinforcement learning approach is more effective and efficient compared with Q-learning and recency-based exploration.

关键词： Learning intelligent systems Adaptive systems Information processing Computers Navigation Simulated annealing intelligent agent Management training Environmental management

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Emotion modeling in autonomic computing

Emotion modeling in autonomic computing

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IEEE International Conference on Cognitive Informatics

作者： Donglei Zhang Yu Pan Zhongzhi Shi Chinese Academy of Sciences Beijing Beijing CN Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China

Autonomic unit is the essential element and the most basic component of autonomic systems. Having the ability of handling emotions is considered to make autonomic unit more intelligent, more communicative and more social. In this article we try to build an emotion model for agents that act as autonomic units in autonomic systems and can self-manage its behavior to achieve the best performance. From four levels we define four models progressively and the top-level model is defined on the foundation of the three below, combining them as a unity. By given arousing axioms theoretically of several typical emotions, the model is proved to be effective for reasoning emotions of autonomic unit, and can be easily extended for dealing with other type of emotions. On this base an architecture of emotional autonomic unit is described. An example shows that emotion plays an important role in intelligent decision making, which proves the validity of the model and the special function in practice.

关键词： Cognition Cognitive science Humans Artificial intelligence Psychology Problem-solving Information processing intelligent sensors Machine intelligence Computational modeling

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A Framework for Error-Tolerant Scheme in Pervasive computing

A Framework for Error-Tolerant Scheme in Pervasive Computing

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International Conference on Education Technology and Computer, ICETC

作者： Jian-quan Ouyang Dian-xi Shi Bo Ding Xiao Li Huai-min Wang Key Laboratory of Intelligent Computing & Information Processing Ministry of Education Xiangtan University China School of Computer National University of Defense Technology Changsha China

Open and dynamic pervasive computing environments make fault detection and error recovery challenging. In this paper, we present a policy based framework for error-tolerant scheme in pervasive computing. As policy-based scheme can separate the business logic (rules) from the controls (programming code) of the implementations, it is typically more flexible and adaptable than non-policy-based approach. Moreover, the policy mechanism is based on not only event-condition-action rules, but also the-Separation-of-Concerns-principle for easily extensible to support the error recovery scheme.

关键词： Pervasive computing Computer architecture Logic programming Computer science education Educational technology Computer errors Fault detection Prototypes Fault tolerant systems Application software

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Filter object categories: employing visual consistency and semisupervised approach

Filter object categories: employing visual consistency and s...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Xi Liu Zhixin Li Zhiping Shi Zhongzhi Shi The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China Chinese Academy of Sciences Beijing Beijing CN

We describe a method for filtering object category from a large number of noisy images. This problem is particularly difficult due to the greater variation within object categories and only a few labeled object images available. Our method deals with it by using visual consistency and semi-supervised approach. The images of one category often share some visual consistency so that the most irrelevant images can be first removed. Among the left images, a voting method is used to obtain more object exemplars with the initial object exemplars manually selected by users. Finally with all the obtained exemplars and those unlabeled images, we create a semi-supervised classifier to rank all the images. We evaluate our method on Berg dataset and demonstrate the precision comparative to the state-of-the-art. Besides, we collect five more categories from Google images to show the effectiveness of the method.

关键词： Voting Support vector machines Support vector machine classification Information filtering Information filters Search engines Training data Laboratories Information processing Computers

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Image Classification Using Structural Sparse Coding Model

Image Classification Using Structural Sparse Coding Model

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International Conference on Natural Computation (ICNC)

作者： Zhiqing Li Zhiping Shi Zhixin Li Zhongzhi Shi The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China Chinese Academy of Sciences Beijing Beijing CN

ISBN: (纸本)9781424445424

Efficient coding hypothesis provides a quantitative relationship between environmental statistics and neural processing. In this paper, we put forward a novel sparse coding model based on structural similarity (SS_SC) for natural image feature extraction. The advantage for our model is to be able to preserve structural information from a scene, which human visual perception is highly adapted for. Using the proposed sparse coding model, the validity of image feature extraction is testified. Furthermore, inspired by Bayesian decision which is extensively used for classification, employing SS_SC we propose an algorithm for image classification. Compared with standard sparse coding (SC) model, the experimental results show that the quality of reconstructed images obtained by our method outperforms the SC method. Moreover, SS_SC model evidently enhances the classification accuracy.

关键词： Image classification Image coding Feature extraction Statistics Layout Humans Visual perception Testing Bayesian methods Classification algorithms

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A Dynamic Description Logic-Based Formalism for RBAC

A Dynamic Description Logic-Based Formalism for RBAC

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International Conference on Computer Sciences and Convergence Information Technology (ICCIT)

作者： Li Ma Shilong Ma Jianghua Lv Yuefei Sui Department of Computer Science Beihang University Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China

ISBN: (纸本)9781424452446

Applications in the open and dynamic environment become more intelligent and complicated. To secure these applications is a big challenge. RBAC model, as a de facto standard in access control field, is widely used in many applications. But the lack of dynamic and formal method to describe RBAC makes the model can't completely adapt to the open and dynamic environment. To solve this problem, we introduce a three level RBAC model which unifies the administrative components, the administrative actions and the regular RBAC components, and also proposes a dynamic description logic, called DDL RBAC , to formalize the three level model. Based on the formal description of RBAC with DDL RBAC , an executable action decision algorithm to guarantee the dynamic consistency of systems is also presented.

关键词： Logic Access control Authorization Data security Information security Mathematical model Permission Information technology Computer science Laboratories

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Effective approach to improve the solving precision of electric field integral equation

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Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics 2009年第11期31卷 2553-2555页

作者： Song, Kai-Hong Zhang, Qing-Hua Wu, Xian-Liang Key Lab. of Intelligent Computing and Signal Processing Anhui Univ. Hefei 230039 China

To improve the accuracy and efficiency for solving the EFIE (electric field integral equation) by the method of moment, a novel scheme is put forward to improve the calculation precision based on the EFIE. Firstly, the inaccurate induced current is obtained by solving the EFIE under coarse mesh grids. Secondly, the scattered magnetic field at any given point on the surface of the object is calculated by the inaccurate induced current. Finally, the accurate induced current at any given point is determined through total magnetic field. The proposed approach is applied to the case of infinitely perfectly electric conducting circular cylinder and square cylinder. The numerical results are presented to demonstrate the high precision and efficiency when the size of mesh grid is large.

关键词： Method of moments

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Detection of Front-View Vehicle with Occlusions Using AdaBoost

Detection of Front-View Vehicle with Occlusions Using AdaBoo...

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International Conference on Information Engineering and Computer Science (ICIECS)

作者： Chunpeng Wu Lijuan Duan Jun Miao Faming Fang Xuebin Wang College of Computer Sciences Beijing University of Technology Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China

ISBN: (纸本)9781424449934

In this paper, we propose a vehicle detection method based on AdaBoost. We focus on the detection of front-view car and bus with occlusions on highway. Samples with different occlusion situations are selected into the training set. By using basic and rotated Haar-like features extracted from the samples in the set, we train an AdaBoost-based cascade vehicle detector. The performance tests on static images and short time videos show that (1) our approach detects cars more effectively than buses (2) the real-time detection of our method on video proceeds at 30 frames per second.

关键词： Vehicle detection Feature extraction Prototypes Detectors Videos Educational institutions Road vehicles Face detection Boosting Laboratories

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A novel two-tier Bayesian based method for hair segmentation

A novel two-tier Bayesian based method for hair segmentation

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IEEE International Conference on Image processing

作者： Dan Wang Shiguang Shan Wei Zeng Hongming Zhang Xilin Chen Chinese Academy and Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China NEC Laboratories Beijing China

In this paper, a novel two-tier Bayesian based method is proposed for hair segmentation. In the first tier, we construct a Bayesian model by integrating hair occurrence prior probabilities (HOPP) with a generic hair color model (GHCM) to obtain some reliable hair seed pixels. These initial seeds are further propagated to their neighborhood pixels by utilizing segmentation results of mean shift, to obtain more seeds. In the second tier, all of these selected seeds are used to train a hair-specific Gaussian model, which are combined with HOPP to build the second Bayesian model for pixel classification. Mean shift results are further utilized to remove holes and spread hair regions. The experimental results illustrate the effectiveness of our approach.

关键词： Bayesian methods Hair Image segmentation Content addressable storage Face detection Information processing Computers National electric code Diversity reception Pixel

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