关键词: 纹理分割, 小波变换, 特征提取, 均值偏移, 模糊c均值

Abstract: An unsupervised texture image segmentation algorithm based on wavelet transform and mean shift algorithm is studied.Firstly，the original image is decomposed in two levels using wavelet transform algorithm.Secondly，the mean shift algorithm is used together with the fuzzy c-means algorithm to divide the data into an appropriate number of clusters in the coarse scale.Thirdly，a peer group corresponding to a clustering center reconstructed from the one of the coarse scale is automatically achieved by use of a threshold function and the Fisher discrimination.And then a texture feature clustering effect is achieved.At last，simulations are performed on the presented algorithm，and the simulation result shows that the presented algorithm not only has high accuracy but also can solve the problems of giving the number of cluster in advance and of sensitivity to initial clustering center of the traditional clustering.

Key words: itexture segmentation, wavelet transform, feature extraction, mean shift, fuzzy c-means