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Image Recognition with Kernel Matching Pursuit Classifier Ensemble Based on Immune Clone |
GOU Shui-Ping, JIAO Li-Cheng, ZHANG Xiang-Rong |
Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education,Institute of Intelligent Information Processing, Xidian University, Xi'an 710071 |
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Abstract An algorithm for kernel matching pursuit classifier (KMPC) ensemble based on immune clone for image recognition is presented to select a subset of the optimal individual from classifier ensemble system and eventually to improve the performance of classifiers. Based on the strong abilities of global optimal search and local search of immune clone algorithm, the proposed method can get ensemble system with better general performance by selecting son kernel matching pursuit from training classifiers. The recognition experiments are made on Brodatz texture image sets and SAR image. The results show that the performance of the proposed algorithm is better than that of the traditional ensemble system and the genetic algorithm based selective ensemble KMPC.
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Received: 03 September 2007
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[1] Pascal V, Yoshua B. Kernel Matching Pursuit. Machine Learning, 2002, 48(1/2/3): 165-187 [2] Hansen L K, Salamon P. Neural Network Ensembles. IEEE Trans on Pattern Analysis and Machine Intelligence, 1990, 12(10): 993-1001 [3] Je H M, Kim D, Yang B S. Human Face Detection in Digital Video Using SVM Ensemble. Neural Processing Letters, 2003, 17(3): 239-252 [4] Jiao Licheng, Li Qing. Kernel Matching Pursuit Classifier Ensemble. Pattern Recognition, 2006, 39(4): 587-594 [5] Krogh A, Vedelsby J. Neural Network Ensembles, Cross Validation, and Active Learning // Touretzky D S, Tesauro G, Leen T K, eds. Advances in Neural Information Processing Systems. Cambridge, USA: MIT Press, 1995, 7: 231-238 [6] Zhou Zhihua, Wu Jianxin, Tang Wei. Ensembling Neural Networks: Many Could Be Better Than All. Artificial Intelligence, 2002, 137(1): 239-263 [7] Li Guozheng, Yang Jie, Kong Ansheng, et al. Clustering Algorithm Based Selective Ensemble. Journal of Fudan University: Natural Science, 2004, 43(5): 689-691,695 (in Chinese) (李国正,杨 杰,孔安生,等.基于聚类算法的选择性神经网络集成.复旦大学学报:自然科学版, 2004, 43(5): 689-691,695) [8] Granitto P M, Verdes P F, Ceccatto H A. Neural Network Ensembles: Evaluation of Aggregation Algorithms. Artificial Intelligence, 2005, 163(2): 139-162 [9] Jiao Licheng, Du Haifeng. Development and Prospect of the Artificial Immune System. Acta Electronica Sinica, 2003, 31(10): 1540-1548 (in Chinese) (焦李成,杜海峰.人工免疫系统进展与展望.电子学报, 2003, 31(10):1540-1548) [10] Mallat S, Zhang Z. Matching Pursuit with Time-Frequency Dictionaries. IEEE Trans on Signal Processing, 1993, 41(12): 3397-3415 [11] Tumer K, Ghosh J. Error Correlation and Error Reduction in Ensemble Classifiers. Connection Science, 1996, 8(3): 385-404 [12] Jiao Licheng, Du Haifeng, Liu Fang, et al. Immunological Computation for Optimization, Learning and Recognition. 1st Edition. Beijing, China: Science Press, 2006 (in Chinese) (焦李成,杜海峰,刘 芳,等.免疫优化计算、学习与识别.第1版.北京:科学出版社, 2006) [13] Dietterich T G. An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization. Machine Learning, 2000, 40(2): 139-157 [14] Meyer F G, Coifman R R. Brushlets: A Tool for Directional Image Analysis and Image Compression. Applied and Computational Harmonic Analysis, 1997, 4(2): 147-187 |
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