Color-texture cosegmentation based on nonlinear compact multi-scale structure tensor and TV-flow

Abstract

Image segmentation is a fundamental problem in computer vision, and the color and texture information are usually both employed to obtain more satisfactory segmentation results. However, the traditional color-texture segmentation methods usually assume that the color-based and texture-based segmentation results are globally consistent, which is not always the case. Sometimes, the color-texture based segmentation results may be worse than some single feature (color or texture) based ones if the consistency constraints are taken into account inappropriately. To address the problem, a graph cuts based color-texture cosegmentation method is proposed in this paper, where just the similarity constrains are considered rather than the global consistencies, and a penalty term is included to adaptively balance the possible local inconsistencies. Additionally, in order to extract the texture features effectively, a comprehensive texture descriptor is designed by integrating the nonlinear compact multi-scale structure tensor (NCMSST) and total variation flow (TV-flow). A large number of segmentation comparison experiments using the synthesis color-texture images and real natural scene images verify the superiorities of our proposed texture descriptor and color-texture cosegmentation method.