Region-Based Image Segmentation Using Texture Statistics And Level-Set Methods

We propose a novel multi-class method for texture segmentation. The segmentation issue is stated as the minimization of a region-based functional that involves a weighted Kullback-Leibler measure between distributions of local texture features and a regularization term that imposes smoothness and regularity of region boundaries. The proposed approach is implemented using level-set methods, and partial differential equations (PDE) are expressed using shape derivative tools introduced in S. Jehan-Besson et al. (2003). As an application, we have tested the method using cooccurrence distributions to segment synthetic mosaics of textures from the Brodatz album, as well as real textured sonar images. These results prove the relevance of the proposed approach for supervised and unsupervised texture segmentation