Artificial immune kernel clustering network for unsupervised image segmentation

作     者：Wenlong Huang , Licheng Jiao Institute of Intelligent Information Processing, Xidian University, Xi’an 710071, China 

作者机构：Institute of Intelligent Information Processing Xidian University Xi’an 710071 China 

出 版 物：《Progress in Natural Science:Materials International》 (自然科学进展·国际材料(英文))

年 卷 期：2008年第18卷第4期

页      面：455-461页

核心收录：

学科分类：08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 

基　　金：2001CB309403 National Natural Science Foundation of China,NSFC: 60372050,60472084 

主　　题：Artificial immune network Kernel mapping Nonsubsampled contourlet transform Unsupervised image segmentation 

摘      要：An immune kernel clustering network (IKCN) is proposed based on the combination of the artificial immune network and the sup- port vector domain description (SVDD) for the unsupervised image segmentation. In the network, a new antibody neighborhood and an adaptive learning coeffcient, which is inspired by the long-term memory in cerebral cortices are presented. Starting from IKCN algo- rithm, we divide the image feature sets into subsets by the antibodies, and then map each subset into a high dimensional feature space by a mercer kernel, where each antibody neighborhood is represented as a support vector hypersphere. The clustering results of the local support vector hyperspheres are combined to yield a global clustering solution by the minimal spanning tree (MST), where a predfined number of clustering is not needed. We compare the proposed methods with two common clustering algorithms for the artificial synthetic data set and several image data sets, including the synthetic texture images and the SAR images, and encouraging experimental results are obtained.