An Adaptive FCM Image Segmentation Algorithm Based on the Feature Divergence
基于特征散度的自适应FCM图像分割算法

Fuzzy C-means (FCM) clustering is one of well-known unsupervised clustering techniques, which has been widely used in automated image segmentation. However, when the classical FCM algorithm is used for image segmentation, there are also some problems, such as weak robustness of distance measure,reguire-ments of setting the initial number of clusters in advance, without considering local image feature. In this paper, an adaptive FCM image segmentation algorithm based on the feature divergence is proposed, which can accomplish image segmentation by importing the feature divergence vector into distance measure, incorporating the cluster validity exponent to ascertain the initial number of clusters automatically and extracting the image feature according to Laws texture measure. Experimental results show that the proposed method is simple and work well for most images (especially for texture images), and has better segmentation effect than the existing FCM image segmentation.