Discriminative Gaussian Mixtures for Interactive Image Segmentation

Recently graph-cut optimization has been extensively explored for interactive image segmentation. In this paper we propose discriminative Gaussian mixtures (DGMs) to boost the performance of graph-cut-based segmentation. Given the user specified pixels, our algorithm analyzes their distributions in color, texture and spatial spaces and produces optimized Gaussian mixtures to set the data cost in the image graph, under the criteria of maximizing the discriminant power. We also show how to assemble novel training data to train DGMs for the link cost in the graph. Experimental results demonstrate that DGMs can noticeably improve the performance of graph-cut segmentation on texture-rich images.