Color image segmentation in a multidimensional space based on clonal selection algorithm

被引：0
|
作者：
Deng X.-Z. [1 ]
Jiao L.-C. [1 ]
Yang S.-Y. [1 ]
Wu Q.-Y. [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：
Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology | 2010年 / 32卷 / 08期
关键词：
Clonal selection; Color image segmentation; Multidensional space; Principal component analysis (PCA);
D O I：
10.3724/SP.J.1146.2009.00922
中图分类号：
学科分类号：
摘要：
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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页码：1792 / 1796
页数：4
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