Image segmentation with texture clustering based JSEG

Abstract

Image segmentation is one of the most crucial techniques in computer vision. In this paper, we propose an improved approach for color image segmentation, Texture clustering based JSEG (T-JSEG). The T-JSEG combines the texture information with the classical JSEG algorithm. We use the Gabor feature clustering to produce texture map and combine it with color map to achieve a new map for J value computing. The color image segmentation result is improved by adding the texture information to the JSEG. Experiments on natural color images of Berkeley Segmentation Database verify that our method is very effective on color image segmentation and more accurate boundaries of regions are found in comparison with the classical JSEG algorithm.