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Isomerous multiple classifier ensemble method with SVM and KMP

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

引用

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

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

引用

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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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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Optimal Approximation of Linear Systems by an Improved Clonal Selection Algorithm

Optimal Approximation of Linear Systems by an Improved Clona...

引用

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

Based on the theory of clonal selection in immunology, by introducing Baldwin effect, an improved clonal selection algorithm, termed as Baldwin clonal selection algorithm (BCSA), is proposed to solve the optimal approximation of linear systems. For engineering computing, the novel algorithm adopts three operations to evolve and improve the population: clonal proliferation operation, Baldwinian learning operation and clonal selection operation. The experimental study on the optimal approximation of a stable linear system and an unstable one show that the approximate models searched by the new algorithm have better performance indices than those obtained by some existing algorithms including the differential evolution algorithm, multi-agent genetic algorithm and artificial immune response algorithm.

关键词： Evolutionary computation

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Improved Clonal Selection Algorithm based on Lamarckian Local Search Technique

Improved Clonal Selection Algorithm based on Lamarckian Loca...

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Congress on Evolutionary Computation

作者： Jie Yang Maoguo Gong Licheng Jiao Lining Zhang 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, we introduce Lamarckian learning theory into the clonal selection algorithm and propose a sort of Lamarckian clonal selection algorithm, termed as LCSA. The major aim is to utilize effectively the information of each individual to reinforce the exploitation with the help of Lamarckian local search. Recombination operator and tournament selection operator are incorporated into LCSA to further enhance the ability of global exploration. We compared LCSA with the clonal selection algorithm (CSA) in solving twenty benchmark problems to test the performance of LCSA. The results demonstrate that LCSA is effective and efficient in solving numerical optimization problems.

关键词： Artificial neural networks Immune system Cloning Optimization Evolutionary computation Benchmark testing Interpolation

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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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The Application of A Novel Target Region Extraction Model Based on Object-Accumulated Visual Attention Mechanism

The Application of A Novel Target Region Extraction Model Ba...

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International Conference on Natural Computation (ICNC)

作者： Jie Xiao Chao Cai Mingyue Ding Chengping Zhou Institute of Pattern Recognition and Artificial Intelligence State Education Commission Key Laboratory for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan China

Combining bottom-up and top-down attention influences, a novel region extraction model which based on object-accumulated visual attention mechanism is proposed in this paper. Compared with early research, the new approach brings in prior information at the proper time, updates scan path dynamically, needs less computational resources and reduces the probability to direct the attention to a less-meaning area. The application to search an airport target in remote sensing image was provided, through which the novel mechanism that how visual attention chose the area was described. Compared with another two region extraction models, experimental results confirm the effectiveness of the approach proposed in this paper.

关键词： Data mining Object detection Computer applications Chaos Pattern recognition Artificial intelligence Laboratories image processing intelligent control Airports

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Co-scheduling model and algorithm of the three gorges-gezhou dam based on GA

Co-scheduling model and algorithm of the three gorges-gezhou...

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7th World Congress on intelligent control and Automation (WCICA 2008), vol.10

作者： Xiaoping Wang Huan Qi Henghui Xiao Xiaopan Zhang Key Laboratory of Image Processing and Intelligent Control Institute of Systems Engineering Huazhong University of Science and Technology Wuhan China

This paper provided a mathematic model for Three Gorges-Gezhou dam co-scheduling problems, based on full analysis of Three Gorges-Gezhou dam's actual needs, to maximize the total throughput of Three Gorges-Gezhou dam, to maximize the utilization ratio of shiplock area and minimize the total navigation shiplock waiting time under eight constraint conditions. Then a scheduling algorithm based on GA was pointed out. The three gorges south lock, Gezhou dam lock, the three gorges north lock were optimization searched separately in the GA algorithm. The scheduling results of the three gorges south lock were taken as the origin of the whole plan period, and also were taken as the basis of the Gezhou dam scheduling together with the ship applied information. The scheduling results of Gezhou dam were regarded as the basis of the three gorges north lock scheduling together with the ship applied information, so repeated, until the optimal scheduling results were given, or the most iterative step was reached. The applied result shows that making a period plan of two dam five lock only needs 2 minutes, and the plan is quite effective according to practical application.

关键词： Genetic algorithm The three gorges-gezhou dam Co-scheduling Mathematical model

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Dim small targets fusion detection in infrared image

Dim small targets fusion detection in infrared image

引用

International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Yu-Qiu Sun Yu Zheng Jin-Wen Tian Jian Liu School of Information and Mathematics Yangtze University Jingzhou China State Education Commission Key Laboratory for Image Processing and Intelligent Control Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan China State Education Commission Key Laboratory for Image Processing and Intelligent Control Department of Electronic Information and Enginery of Huazhong University of Science and Technology Wuhan China

To infrared images, the contrast of target and background is low, dim small targets have no concrete shapes and their textures cannot be reliable predicted. The paper puts forward a novel algorithm to fuse mid-wave and long-wave infrared images and detect targets. Firstly, the source images are decomposed by wavelet transformation. In usual, targets in infrared images are man-made, and their fractal dimension is different comparing with natural background. In wavelet transformation domain high-frequency part, we calculate local fractal dimension and set up fusion rule to merge corresponding sub-images of two matching source images. In low-frequency, we extract local maximum gray level to fuse them. Then reconstruct image by wavelet inverse transformation and obtain fused result image. In fusion results, the contrast between targets and background has obvious changes. And targets can be detected using contrast threshold. The experimental results show that the method proposed in this paper using wavelet transformation fractal dimension to fuse dual band infrared images, and then detect targets is better than using mid-wave or long -wave infrared images detect targets alone.

关键词： Fractals Wavelet transforms Pixel Object detection Fuses Approximation methods Manganese

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Unsupervised texture image segmentation using multiobjective evolutionary clustering ensemble algorithm

Unsupervised texture image segmentation using multiobjective...

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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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