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Congress on Evolutionary Computation

作者： Xiaoxue Qian Xiangrong Zhang Licheng Jiao Wenping Ma Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Institute of Intelligence Information Processing Xidian University Xi'an China

Multiobjective evolutionary clustering approach has been successfully utilized in data clustering. In this paper, we propose a novel unsupervised machine learning algorithm namely multiobjective evolutionary clustering ensemble algorithm (MECEA) to perform the texture image segmentation. MECEA comprises two main phases. In the first phase, MECEA uses a multiobjective evolutionary clustering algorithm to optimize two complementary clustering objectives: one based on compactness in the same cluster, and the other based on connectedness of different clusters. The output of the first phase is a set of Pareto solutions, which correspond to different tradeoffs between two clustering objectives, and different numbers of clusters. In the second phase, we make use of the meta-clustering algorithm (MCLA) to combine all the Pareto solutions to get the final segmentation. The segmentation results are evaluated by comparing with three known algorithms: K-means, fuzzy K-means (FCM), and evolutionary clustering algorithm (ECA). It is shown that MECEA is an adaptive clustering algorithm, which outperforms the three algorithms in the experiments we carried out.

关键词： Evolutionary computation

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Support vector novelty detection with dot product kernels for non-spherical data

Support vector novelty detection with dot product kernels fo...

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International Conference on Information and Automation (ICIA)

作者： Li Zhang Zhou Weida Ying Lin Licheng Jiao Institute of Intelligent Information Processing & Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Xi'an China

In this paper, a variant of support vector novelty detection (SVND) with dot product kernels is presented for non-spherical distributed data. Firstly we map the data in input space into a reproducing kernel Hilbert space (RKHS) by using kernel trick. Secondly we perform whitening process on the mapped data using kernel principal component analysis (KPCA). Finally, we adopt SVND method to train and test whitened data. Experiments were performed on artificial and real-world data.

关键词： Kernel Distributed databases Training Covariance matrix Support vector machines Training data Eigenvalues and eigenfunctions

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The forward problem algorithm based on modified element free Galerkin method for bioluminescence tomography

The forward problem algorithm based on modified element free...

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

作者： Chenghu Qin Jie Tian Kai Liu Yakang Dai Medical Image Processing Group Key Laboratory of Complex Systems and Intelligence Science Institute of Automation Chinese Academy and Sciences China

As an emerging and promising molecular imaging modality, bioluminescence tomography (BLT) can reconstruct the internal light source with the photon fluence on the small animal surface to reveal non-invasive molecular and cellular activities directly. In order to obtain higher precision and better spatial resolution in source reconstruction, the solution accuracy for the forward problem of BLT should be improved as high as possible. In this contribution, we present a modified element free Galerkin method (MEFGM) to calculate photon propagation in the biological tissue. This method is based on moving least squares (MLS) approximation which requires only a series of nodes in the region of interest, so complicated meshing task can be avoided compared with finite element method (FEM). Furthermore, MLS shape functions are further modified to satisfy the delta function property, which can simplify the processing of boundary conditions in comparison with traditional meshless methods. Finally, the numerical simulation experiments demonstrate the effectiveness and feasibility of this proposed method by comparing the solution of MEFGM with that of FEM.

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Small universal spiking neural P systems with exhaustive use of rules

Small universal spiking neural P systems with exhaustive use...

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International Conference on Bio-Inspired Computing: Theories and Applications, BICTA

作者： Xingyi Zhang Yun Jiang Linqiang Pan Key Laboratory of Image Processing and Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan Hubei China

We consider the problem of looking for small universal spiking neural P systems with exhaustive use of rules, which was formulated as an open problem by Gheorghe Paun in a survey paper. Here, spiking neural P systems are used in two versions: as devices computing functions and as devices generating sets of numbers, with two ways of encoding the result of a computation. As devices of computing functions, if we associate the result with the distance between the first two spikes emitted by the output neuron, we produce a universal computing spiking neural P system with exhaustive use of rules (without delay) having 125 neurons; if we introduce the usual way of defining the result of a computation in membrane systems to encode the result, namely, the number of spikes emitted during a computation, then a universal computing system (without delay) with 126 neurons is also obtained in the sense of the exhaustive use of rules. For spiking neural P systems used as generators of sets of numbers, we construct a universal system (without delay) by using 128 neurons under the first way of defining the computation result, and a system (without delay) by using 127 neurons under the second way of defining the computation result.

关键词： Neurons Registers Tin Delay Generators Distance measurement Biomembranes

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Improved Clonal Selection Algorithm based on Baldwinian Learning

Improved Clonal Selection Algorithm based on Baldwinian Lear...

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Congress on Evolutionary Computation

作者： Lining Zhang Maoguo Gong Licheng Jiao Jie Yang Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an China

In this paper, based on Baldwin effect, an improved clonal selection algorithm, Baldwin clonal selection algorithm, termed as BCSA, is proposed to deal with complex multimodal optimization problems. BCSA evolves and improves antibody population by three operations: clonal proliferation operation, Baldwinian learning operation and clonal selection operation. By introducing Baldwin effect, BCSA can make the most of experience of antibodies, accelerate the convergence, and obtain the global optimization quickly. In experiments, BCSA is tested on four types of functions and compared with the clonal selection algorithm and other optimization methods. Experimental results indicate that BCSA achieves a good performance, and is also an effective and robust technique for optimization.

关键词： Evolutionary computation

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Needle segmentation using 3D quick randomized hough transform

Needle segmentation using 3D quick randomized hough transfor...

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1st International Conference on Intelligent Networks and Intelligent Systems, ICINIS 2008

作者： Qiu, Wu Ding, Mingyue Yuchi, Ming Institute for Pattern Recognition and Artificial Intelligence Image Processing and Intelligence Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan Hubei China Department of Biomedical Engineering School of Life Science and Technology Huazhong University of Science and Technology Wuhan Hubei China

ISBN: (纸本)9780769533919

A quick 3D needle segmentation algorithm for 3D US data is described in this paper. The algorithm includes the 3D Quick Randomized Hough Transform (3DGHT), which is based on the 3D Randomized Hough Transform and coarse-fine searching strategy. We tested it with water phantom. The results show that our algorithm works well in 3D US images with angular deviation less than 1 and position deviation less than 1mm, and the computational time of segmentation with 35MB data is within 1s. © 2008 IEEE.

关键词： Hough transforms

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Support vectors pre-extracting for support vector machine based on K nearest neighbour method

Support vectors pre-extracting for support vector machine ba...

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International Conference on Information and Automation (ICIA)

作者： Li Zhang Ning Ye Weida Zhou Licheng Jiao Institute of Intelligent Information Processing & Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education University of Xidian Xi'an Shaanxi China

Support vector machine, a universal method for learning from data, gains its development based on statistical learning theory. It shows many advantages in solving nonlinearly small sample and high dimensional problems of pattern recognition. Only a part of samples or support vectors (SVs) plays an important role in the final decision function. But SVs could not be obtained in advance until a quadratic programming is performed. In this paper, we use K-nearest neighbour method to extract a boundary vector set which may contain SVs. The number of the boundary set is smaller than the whole training set. Consequently it reduces the training samples, speeds up the training of support vector machine.

关键词： Training Support vector machines Kernel Distance measurement Classification algorithms Polynomials Support vector machine classification

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Isomerous multiple classifier ensemble method with SVM and KMP

Isomerous multiple classifier ensemble method with SVM and K...

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International Conference on Audio, Language and image processing, ICALIP

作者： Shuiping Gou Shasha Mao Licheng Jiao Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an China

A method for multi-classifier ensemble of Support Vector Machine ensemble (SVMs) and Kernel Matching Pursuit Ensemble (KMPs) is proposed. Support Vector Machine has advantage in solving classification problem of high dimension and small size dataset, and Kernel Matching Pursuit has almost classified performance and the more sparsely solution as comprised with the SVM. So the SVM and the KMP are mix boosted in this paper, which can decrease generalization errors of the single classifier ensemble and improve ensemble classification accuracy by increasing diversity between ensemble individuals. The experiments show that the proposed method can shorten running time and improve classification accuracy compared with individual SVMs or KMPs.

关键词： Support vector machines Classification algorithms Accuracy Kernel Training Matching pursuit algorithms Machine learning

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Lossy to lossless image compression based on reversible integer DCT

Lossy to lossless image compression based on reversible inte...

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IEEE International Conference on image processing

作者： Lei Wang Jiaji Wu Licheng Jiao Li Zhang Guangming Shi Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an China

A progressive image compression scheme is investigated using reversible integer discrete cosine transform (RDCT) which is derived from the matrix factorization theory. Previous techniques based on DCT suffer from bad performance in lossy image compression compared with wavelet image codec. And lossless compression methods such as IntDCT, I2I-DCT and so on could not compare with JPEG-LS or integer discrete wavelet transform (DWT) based codec. In this paper, lossy to lossless image compression can be implemented by our proposed scheme which consists of RDCT, coefficients reorganization, bit plane encoding, and reversible integer pre- and post-filters. Simulation results show that our method is competitive against JPEG-LS and JPEG2000 in lossless compression. Moreover, our method outperforms JPEG2000 (reversible 5/3 filter) for lossy compression, and the performance is even comparable with JPEG2000 which adopted irreversible 9/7 floating-point filter (9/7F filter).

关键词： image coding Discrete cosine transforms Transform coding Filters Performance loss Discrete wavelet transforms Codecs image reconstruction Laboratories Information processing

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Neutrosophic combination rules based on dempster-shafer theory and Dezert-Smarandache theory

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Information Technology Journal 2008年第7期7卷 1023-1029页

作者： Gao, Jian Huang, Xinhan Wang, Min Li, Xinde Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Department of CSE HUST Wuhan 430074 China School of Automation Southeast University Nanjing 210096 China

Several neutrosophic combination rules based on the Dempster-Shafer theory (DST) and Dezert-Smarandache theory (DSmT) are presented in this study. The new information fusing approaches proposed the neutrosophic belief assignment to represent the evidences and connect the DST and DSmT with neutrosophic theory. Neutrosophic theory used the components truth, indeterminacy and falsity to represent any idea, which provides a better approach to express the real world. The DST-based and DSmT-based combination rules can fuse information more effectively. The new combination rules utilized the advantages of DST, DSmT and neutrosophic theory. They are more adapted to describing the human mind and helping to make correct decisions. To fuse the information expressed in natural language, the qualitative neutrosophic combination rules of information have been proposed in this study too. Some application examples have been given to support the proposed combination rules. Compared with the original DST-based and DSmT-based combination rules, the neutrosophic combination rules can provide more useful information and have wider application field. © 2008 Asian Network for Scientific Information.

关键词： Information fusion

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