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Volume 32 Issue 8
Sep.  2010
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Deng Xiao-Zheng, Jiao Li-Cheng, Yang Shu-Yuan, Wu Qiu-Yi. Color Image Segmentation in a Multidimensional Space Based on Clonal Selection Algorithm[J]. Journal of Electronics & Information Technology, 2010, 32(8): 1792-1796. doi: 10.3724/SP.J.1146.2009.00922
Citation: Deng Xiao-Zheng, Jiao Li-Cheng, Yang Shu-Yuan, Wu Qiu-Yi. Color Image Segmentation in a Multidimensional Space Based on Clonal Selection Algorithm[J]. Journal of Electronics & Information Technology, 2010, 32(8): 1792-1796. doi: 10.3724/SP.J.1146.2009.00922

Color Image Segmentation in a Multidimensional Space Based on Clonal Selection Algorithm

doi: 10.3724/SP.J.1146.2009.00922
  • Received Date: 2009-06-26
  • Rev Recd Date: 2010-05-13
  • Publish Date: 2010-08-19
  • A novel color image segmentation method is proposed in this paper. Multidimensional space is defined by using the PCA technique to computing the most discriminating color components for a given color image among a set of conventional color spaces. Then, training samples for every region in the color image is selected and these samples is trained by clonal selection algorithm to obtain clustering center of every region. Finally, output the segmentation result according to these clustering centers. Due to the nonlinear classification property of clonal selection algorithm and adaptive definition of a multidimensional space for a given color image, the segmentation result can be obtained accurately and quickly. In experiments, different color images are used to test the performance of the suggested method. The result indicated that this method performs more robustness and adaptability.
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  • Cheng H D, Jiang X H, Sun Y, and Wang J. Color imagesegmentation: aadvances and prospects[J]. PatternRecognition, 2001, 34(12): 2259-2281.[2]Trussel H J, Saber E, and Vrhel M. Color image processing:basics and special issue overview[J]. IEEE Signal ProcessingMagazine, 2005, 22(1): 14-22.[3]Kurugollu F, Sankur B, and Harmanci A E. Color imagesegmentation using histogram multithresholding andfusion[J].Image and Vision Computing.2001, 19(13):915-928[4]Cheng F Y and Shou-Xian. Automatic seeded region growingfor color image segmentation[J].Image and Vsion Computing.2005, 23(10):877-886[5]Koschan A and Abidi M. Detection and classification of edgesin color images[J].IEEE Signal Processing Magazine.2005,22(1):64-73[6]Hung Wen-liang, Yang Min-shen, and Chen De-hua.Bootstrapping approach to feature-weight selection in fuzzyc-means algorithms with an application in color imagesegmentation[J].Patter Recognition Letters.2008, 29(9):1317-1325[7]马文萍, 焦李成, 尚荣华. 免疫克隆SAR图像分割算法[J].电子与信息学报.2009, 31(7):1749-1752浏览[8]Alain Tremeau. Color classification in a multidimensionalcolor space[C]. IEEE International Symposium on SignalProcessing and Information Technology, Cairo, Egypt, Dec.15-18, 2007: 819-824.[9]De Castro L N and Timmis J. Artificial immune systems: ANew Computational Intelligence Approach[M]. Berlin:Speringer-Verlag, 2002: 15-16.[10]Forrest S, Perelson A S, and Alledn L, et al.. Self-nonselfdiscrimination in a computer[C]. Proceedings of IEEESymposium on Research in Security and Privacy, Oakland,CA, USA, May 16-18, 1994: 202-212.[11]De Castro L N and Von Zuben F J. Learning andoptimization using the clonal selection principle[J].IEEETransactions on Evolutionary Computation.2002, 6(3):239-251[12]Farmer J D, Packard N H, and Perelson A S. The immunesystem, adaptation, and machine learning[J]. Physical D,1986, 2(1): 187-204.[13]焦李成, 杜海峰, 刘芳. 免疫优化计算、学习与识别[M]. 北京:科学出版社, 2006: 12-34.Jiao Li-cheng, Du Hai-feng, and Liu Fang. ImmunologicalComputation for Optimization, Learning and Recognition[M].Beijing: Science Press, 2006: 12-34.
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