This paper proposes an interactive texture segmentation method based on GrabCut. In order to extract the texture features effectively, a new texture descriptor is designed by integrating the nonlinear compact multi-scale structure tensor (NCMSST) and total variation flow (TV-flow). NCMSST is constructed by means of dimension reduction and nonlinear filtering for the traditional multi-scale structure tensor (MSST), and TV-flow is used to compensate the loss of large-scale texture descriptive ability by extracting local scale information. Then, the GrabCut framework is applied to deal with the texture image segmentation, and the corresponding experiment results demonstrate the superiority of our proposed texture descriptor in terms of high efficiency and accuracy.
In this paper, an interactive image segmentation method with high accuracy and low time consumption is developed. The method regards the "shrinking bias" issue of traditional graph cuts as a benefit and makes full use of it by using the deformed multiresolution technique, which can also provide a partial solution to it incidentally. The input image is first coarsened deformedly to some low resolutions with the different width-length ratios simultaneously, and then GrabCut method is applied on them to obtain the different segmentations. To sum up the differences of these coarse labeling results, a "weighted map" is constructed to present possibilities of each area for foreground or background, which can describe the object in details with high accuracy. Finally, the "weighed map" is used to refine the trimap for building the more accurate Gaussian mixture models and graph cuts model to assign the final segmentation labeling. Our method is evaluated on two famous benchmarks extensively. The experimental results indicate that our proposed method has the higher segmentation accuracy as well as the lower time consumption when compared with the GrabCut and even the recently proposed OneCut.