Interactive image segmentation via superpixel pairs probabilistic diffusion
Yu Xia, Tao Wang, Zexuan Ji · 2020
This paper introduces a new interactive image segmentation approach based on global pairwise relationship. Many conventional interactive image segmentation methods only consider local relationship of neighboring pixels or unary probability of pixels, which results in the sensitivity to seeds. To overcome this drawback, we utilizes the pixel-pairwise relationship to obtain the global pairwise relationship of pixels. The constructed global binary probability is used to estimate the labels of pixels. In order to improve the computational efficiency, we further replace pixels with superpixels and use the binary global relationship of superpixels for image segmentation. Our method makes full use of global binary information and has stronger robustness to limited seeds information. The superior performances of our method are demonstrated in the experiments on the Berkeley segmentation dataset and Microsoft GrabCut database.