关键词: 模糊C均值聚类, 二维熵, 图像分割

Abstract: In this paper, an image segmentation algorithm for combining fast two-dimensional entropy and weighted fuzzy C-means（FCM） clustering is proposed. The centers of the object and the background are obtained by applying fast two-dimensions entropy algorithm. Then, the influence of every sample on the classification is characterized by the gray difference between the sample and its neighborhood samples. At last, the segmentation is obtained by weighted fuzzy C-means clustering algorithm. The new algorithm can solve the question that the traditional FCM clustering algorithm is sensitive to the initial value. Moreover, it can overcome the shortage that the traditional clustering algorithm is equally partition to the sample set. The experimental result shows that the algorithm not only has good convergence, but also can effectively segment the target from its background. The new algorithm has the important practical application value.

Key words: fuzzy C-means clustering, two-dimensional entropy, image segmentation