Unsupervised multiphase color-texture image segmentation based on variational formulation and multilayer graph

This paper proposes an unsupervised variational segmentation approach of color-texture images. To improve the description ability, the compact multi-scale structure tensor, total variation flow, and color information are integrated to extract color-texture information. Since heterogeneous image object and nonlinear variation exist in color-texture image, it is not appropriate to use one single/multiple constant in the Chan and Vese (CV) model to describe each phase [1,2]. Therefore, a multiphase successive active contour model (MSACM) based on the multivariable Gaussian distribution is presented to describe each phase. As geodesic active contour (GAC) has a stronger ability in capturing boundary. To inherit the advantages of edge-based model and region-based model, we incorporate the GAC into the MSACM to enhance the detection ability for concave edge. Although multiphase optimization of our proposed MSACM is a NP hard problem, we can discretely and approximately solve it by a multilayer graph method. In addition, to segment the color-texture image automatically, an adaptive iteration convergence criterion is designed by incorporating the local Kullback-Leibler distance and global phase label, so that we can control the segmentation process converges. Comparing to state-of-the-art unsupervised segmentation methods on a substantial of color texture images, our approach achieves a significantly better performance on capture ability of homogeneous region/smooth boundary and accuracy.