Vector-valued Chan-Vese model driven by local histogram for texture segmentation

The Chan-Vese model is one of the most popular region-based active contours, and its vector-valued extension is also powerful for multichannel images. Very recently, the histogram is introduced into the Chan-Vese model due to the effectiveness of histogram to model region information. Motivated by the fact that the histogram is also a powerful tool to characterize texture, it is introduced into the vector-valued Chan-Vese model for texture segmentation in this work. In order to determine an optimal number of bins in the histogram, a Bayesian method is adopted. Experiments are conducted and the results show that the proposed strategy is effective for texture segmentation.