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5th International Conference on Natural Computation, ICNC 2009

作者： Zou, Baixian Miao, Jun Yang, Xiaoling Duan, Lijuan Qiao, Yuanhua Department of Information Science and Technology College of Arts and Science of Beijing Union University Beijing 100083 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Tianjin Academy of Agricultural Science Tianjin 300192 China College of Computer Science and Technology Beijing University of Technology Beijing 100022 China College of Applied Science Beijing University of Technology Beijing 100022 China

ISBN: (纸本)9780769537368

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. © 2009 IEEE.

关键词： Computational complexity

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Three-dimensional source reconstruction for bioluminescence tomography using the LSQR method with preconditioning

Three-dimensional source reconstruction for bioluminescence ...

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MIPPR 2009 - Medical Imaging, Parallel processing of Images, and Optimization Techniques: 6th International Symposium on Multispectral Image processing and Pattern Recognition

作者： Yu, Jingjing Liu, Fang Jiao, Licheng He, Xiaowei School of Computer Science and Technology Xidian University Xi'an 710071 China Scholl of Physics and Information Technology Shaanxi Normal University Xi'an 710062 China Key Laboratory of Intelligent Perception and Image Understanding Ministry of Education of China Xi'an 710071 China Institute of Intelligent Information Processing Xidian University Xi'an 710071 China School of Information Science and Technology Northwest University Xi'an 710069 China

ISBN: (纸本)9780819478085

As a novel optical molecular imaging modality, Bioluminescence Tomography (BLT) aims at quantitative reconstruction of the bioluminescent source distribution inside the biological tissue from the optical signals measured on the living animal surface, which is a highly ill-posed inverse problem. In this paper, with the finite element method solving the diffusion equation, an iterative regularization algorithm, referred to as least square QR-factorization (LSQR), is applied to the inverse problem in BLT. The affect of the preconditioning strategy on the LSQR method (PLSQR) for BLT is also investigated. The Simulations with a heterogeneous mouse chest phantom demonstrate that by incorporating a priori knowledge of the permissible source region, the LSQR method can reconstruct the source accurately and stably. Moreover, by employing preconditioning strategy, PLSQR outperforms LSQR in terms of source power density and convergence speed. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Inverse problems

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Learning image semantics with latent aspect model

Learning image semantics with latent aspect model

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IEEE International Conference on Multimedia and Expo (ICME)

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

Automatic image annotation has become an important and challenging problem due to the existence of semantic gap. In this paper, we present an approach based on probabilistic latent semantic analysis (PLSA) to accomplish the tasks of semantic image annotation and retrieval. In order to model training images precisely, we employ two PLSA models to capture semantic information from visual and textual modalities respectively. Then an adaptive asymmetric learning approach is proposed to fuse aspects which are learned from both modalities. For each image document, the weight of each modality is determined by its contribution to the content of the image. Consequently, the two models are linked with the same distribution over aspects. This structure can predict semantic annotation for an unseen image because it associates visual and textual modalities properly. Finally, we compare our approach with several previous approaches on a standard Corel dataset. The experiment results show that our approach performs more effective and accurate.

关键词： Image retrieval Image databases Fuses Indexing Feature extraction Spatial databases Visual databases Hidden Markov models Laboratories information processing

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Skeleton-based fast path planning for UAV

Skeleton-based fast path planning for UAV

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MIPPR 2009 - Remote Sensing and GIS Data processing and Other Applications: 6th International Symposium on Multispectral Image processing and Pattern Recognition

作者： Liu, Xin Zhou, Chengping Ding, Mingyue Cai, Chao Institute for Pattern Recognition and Artificial Intelligence National Lab. for Multi-spectral Information Processing Technologies Huazhong University of Science and Technology Wuhan 430074 China College of Life Science and Technology Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9780819478092

A new path planning method for UAV in static workspace is presented. The method can find a nearly optimal path in short time which satisfies the UAV kinematic constraints. The method makes use of the skeletons to construct the graph of the planning space considering the configuration of the obstacles and utilizes the graph to find a shortest collision-free path, and a novel technique is utilized to convert the free path into a feasible path. The method can be applied to different applications and easy to be implemented. Experimental results showed that the path planning can be done in a fraction of second on a contemporary workstation (2-3 seconds) under the condition of satisfying the kinematic constraints. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Motion planning

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A Comparative Study of PCA, LDA and Kernel LDA for Image Classification

A Comparative Study of PCA, LDA and Kernel LDA for Image Cla...

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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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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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Adaptive Synchronization between Two Different Complex Networks with Time-Varying Delay Coupling

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Chinese Physics Letters 2009年第6期26卷 61-64页

作者：陈建芮焦李成吴建设王晓华 Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligence Information Processing Xidian University Xi'an 710071 College of Science Inner Mongolia University of Technology Inner Mongolia 010051

A new general network model for two complex networks with time-varying delay coupling is presented. Then we investigate its synchronization phenomena. The two complex networks of the model differ in dynamic nodes, the number of nodes and the coupling connections. By using adaptive controllers, a synchronization criterion is derived. Numerical examples are given to demonstrate the effectiveness of the obtained synchronization criterion. This study may widen the application range of synchronization, such as in chaotic secure communication.

关键词： gamma-ray bursts gamma-rays relativity

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