The Research Of Textrue Segmentation Alrogithm Based On Wavelet And Fuzzy Clustering

Texture is an important feature in many nature sceneries, remote sensing, medical image and many other types of images. Perception texture plays a crucial role for activities of recognition and representation in the human vision system. Texture analysis is a main and useful area of study in pattern recognition. Texture segmentation is an important and difficult problem in the texture analysis; texture feature extraction and classification are crucial in the texture segmentation.In this paper, we made an investigation into texture feature extraction and classification based on wavelet analysis theory and fuzzy clustering method. The research work of this paper can be classified as the following two aspects.First, according to human vision perception theory, we studied texture feature extraction based on wavelet multi-scale analysis. After reviewing wavelet theory, pyramid wavelet decomposition was introduced to texture representation, we discussed the problem of optimal window size for texture feature extraction method; the problem of local statistical feature value refinement has been studied, and a refinement method has been given which can decrease the boundary effect in calculating the feature value.Second, in view of the fuzzy and stochastic characteristics of the human vision system, we studied fuzzy clustering algorithm. Based on fuzzy clustering algorithm, we studied the objective function of the traditional fuzzy c-means algorithm and proposed a modified objective function for FCM; we discussed clustering validity problem, and a texture segmentation method based on adaptive FCM has been constructed by the guidance of fuzzy clustering validity.