Texture Segmentation with Wavelet Transform and Feature Weighting
利用小变换和特征加权进行纹理分割

To improve the accuracy of boundary locations and region homogeneity as well as to reduce the error rate in texture image segmentation, a novel approach based on wavelet-transform and using feature weighting is proposed in this paper. This new technique contains three consecutive stages: feature extraction, pre-segmentation and post-segmentation. In the feature extraction stage, texture features are extracted by using the pyramid-structured wavelet transform. The original image is then segmented initially using the means clustering algorithm in the pre-segmentation stage. According to the pre-segmentation results, the extracted features are weighted and the pre-segmented image is further processed with a minimum distance classifier in the post-segmentation stage to finally get the segmented image. All technical points are clearly described and presented in detail. Some segmentation experiments with different Brodatz's texture images are performed to test the performance of the new technique and are also included. Compared with a typical traditional method, the present approach shows visible improvements both in diminishing segmentation error, and in increasing boundary precision and region harmony.