Feature extraction and soft segmentation of texture images

In this paper, we propose an efficient method for texture image segmentation. First we extract four feature channels smoothed with the total variation (TV) flow. Then we propose a soft segmentation model based on the Chan-Vese model by adding a weight in the arc length term and using a soft membership function in stead of level set function to represent the region. We derive a fast algorithm using the Additive Operator Scheme (AOS) and Chambolle's fast dual projection method. Experimental results on texture and Synthetic Aperture Radar (SAR) images show the effectiveness of our algorithm.