A Histogram-Based Chan-Vese Model Driven by Local Contrast Pattern for Texture Image Segmentation

This paper proposes a novel local contrast pattern (LCP) to drive the histogram-based Chan-Vese (CV) model for texture image segmentation. The local contrast pattern has two maps, differential contrast map and orientation map, which are well suited to describe texture structure, especially the texture orientation information. In order to enable the extraction of accurate local texture features, a truncated Gaussian kernel function is also incorporated into the improved model. Then, a novel histogram-based CV model is guided by the LCP feature maps and a truncated Gaussian kernel to obtain the texture segmentation. Moreover, we verify the robustness for illumination, noise and initialization of the proposed model in level set framework. Experiments and comparisons demonstrate that the proposed model is effective on various types of image for texture segmentation.