Color Image Segmentation in a Multidimensional Space Based on Clonal Selection Algorithm
基于克隆选择和多重空间构造的彩色图像分割

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.