Image Segmentation Based on Fussing Multi-feature and Spatial Spectral Clustering

A new method for image feature extraction and segmentation is proposed in this paper. Abundant contour feature information of the image is expressed by contourlet transform while texture feature of the image is described by wavelet transform and Gray Level Co-occurrence Matrix (GLCM). The three type feature information compose feature matrix. The presented method describes different image information using different characterization transform and keeps well useful original image information. Then we select spectral mapping to simply the feature matrix and gain distributed datasets. And the images are segmented by fuzzy clustering algorithm with spatial constraints, which can improve the robustness of the proposed method to the images containing noise. Simulation results of the texture images and Synthetic Aperture Radar (SAR) images show the proposed method had higher accuracy compared with traditional spectral clustering.