Immune spectral clustering algorithm for image segmentation

被引：3
|
作者：
Zhang X.-R. [1 ]
Qian X.-X. [1 ]
Jiao L.-C. [1 ]
机构：
[1] Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Institute of Intelligent Information Processing, Xidian University
来源：
Ruan Jian Xue Bao/Journal of Software | 2010年 / 21卷 / 09期
关键词：
Dimension reduction; Image segmentation; Immune spectral clustering; Nyströms method; Spectral clustering;
D O I：
10.3724/SP.J.1001.2010.03581
中图分类号：
学科分类号：
摘要：
An image segmentation approach based on immune spectral clustering algorithm, is proposed, in which the dimension reduction ability of the spectral clustering is used to attain the distribution of data in the mapping space. Next, a new immune clonal clustering algorithm is proposed to cluster the sample points in the mapping space. Compact input with low-dimension for immune clonal clustering is obtained after spectral mapping, and the immune clonal clustering algorithm, characterized by its rapid convergence to global optimum and minimal sensitivity to initialization, can obtain good clustering results. To efficiently apply the algorithm to image segmentation, Nyström method is used to reduce the computation complexity. Experimental results on synthetic texture images and SAR images show the validity of the algorithm in image segmentation. © by Institute of Software, the Chinese Academy of Sciences. All rights reserved.
引用
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页码：2196 / 2205
页数：9
相关论文
共 21 条
  • [11] Du H.F., Jiao L.C., Wang S.A., Clonal opetator and antibody clone algorithm, Proc. of the 1st Int'l Conf. on Machine Leaning and Cybernetics, pp. 506-510, (2002)
  • [12] Du H.F., Jiao L.C., Gong M.G., Liu R.C., Adaptive dynamic clone selection algorithm, Proc. of the Int'l Conf. on Rough Sets and Current Trends in Computing, pp. 768-773, (2004)
  • [13] Fowlkes C., Belongie S., Chung F., Malik J., Spectral grouping using the Nyström method, IEEE Trans. on Pattern Analysis and Machine Intelligence, 26, 2, pp. 214-225, (2004)
  • [14] Kannan R., Vempala S., Vetta A., On clusterings-good, bad and spectral, Journal of the ACM, 51, 3, pp. 497-515, (2004)
  • [15] Ng A.Y., Jordan M.I., Weiss Y., On spectral clustering: Analysis and an algorithm, Advances in Neural Information Processing Systems, 14, pp. 849-856, (2002)
  • [16] Meila M., Shi J.B., Learning segmentation by random walks, Advances in Neural Information Processing Systems, 13, pp. 837-879, (2000)
  • [17] Meila M., Shi J.B., A random walks view of spectral segmentation, Proc. of the Artificial Intelligence and Statistics AIS-TATS, (2001)
  • [18] Hall L.O., Ozyurt B., Bezdek J.C., Clustering with a genetically optimized approach, IEEE Trans. on Evolutionary Computation, 3, 2, pp. 103-112, (1999)
  • [19] Arbib M.A., The Handbook of Brain Theory and Neural Networks, (2002)
  • [20] Rignot E., Kwok R., Extraction of textural features in SAR images: Statistical model and sensitivity, Proc. of the Int'l Groscience and Remote Sensing Symp, pp. 1979-1982, (1990)
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