Image segmentation using dual distribution matching

Abstract

We propose an image segmentation method that divides an image into foreground and background regions when the approximate color distributions for these regions are given. Our approach was inspired by global consistency measures that directly evaluate the similarity between a given distribution and the distribution of the resulting segmentation, which were recently proposed in order to overcome the limitations of traditional pixelwise (local) consistency measures. The main feature of our proposal is that it uses two (foreground and background) input distributions, which increases the robustness compared to previous studies. To achieve this, we formulated a new mathematical model that describes the consistencies between the two input distributions and the segmentation, in which weighting parameters for the two distribution matching terms are set to be approximately proportional to the size of the foreground and background areas. We call this dual distribution matching (DDM). We also derived an optimization method that uses graph cuts. Experimental results that show the effectiveness of our method and comparisons between local and global consistency measures are presented.