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

• A new color texture feature is constructed.
• Multiphase successive active contour model (MSACM) is proposed.
• MSACM can be discretely optimized by multilayer graph.
• The combined energy of local and global is iteratively optimized.
• Performances are assessed through the qualitative and quantitative comparisons.

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.