TEXTURE SEGMENTATION BASED ON LOCAL FEATURE HISTOGRAMS

This paper presents a convex vector-valued active contour model for texture segmentation. This model uses histograms of the semi-local region descriptor and image intensity for measuring the similarity of image regions. We use the Quadratic-Chi histogram distance to compare the dissimilarity of histograms. Quadratic-Chi histogram distance is a cross-bin distance that matches perceptual similarity better than the bin-to-bin distance (such as Kullback-Leibler divergence and Bhattacharyya distance). Then we use a primal-dual method to solve the minimization problem. Experimental results for real images show the effective of the proposed method.