关键词: 图像分割, 聚类, PCM聚类, 类间距极大化, 纹理图像

Abstract: As one kind of image segmentation methods, fuzzy C-means（FCM）clustering is an effective and simplest method. As a variant of FCM, Possibility C-means algorithm（PCM） algorithm has better clustering performance and?the?enhanced?interpretability?based?on?the?probability?theory. However, both FCM and PCM are sensitive to the noise and outliers due to the membership degree of constraint. To solve the above problems, a new clustering algorithm called maximum the between-cluster（MPCM） is proposed. Which takes the dissimilar penalty term between centers into consideration, widened the distance between the center of the classes, to achieve the best classification of pixels in the image through the dissimilarity parameters [λ]. The experiment results of synthetic texture image, real image and real images with salt and pepper noise shows that the new algorithm is better than the conventional clustering analysis method on the image segmentation technique.

Key words: image segmentation, clustering, Possibility C-means（PCM）, maximum the between-cluster, texture image