Sequential Convex Relaxation for Mutual Information-Based Unsupervised Figure-Ground Segmentation

We propose an optimization algorithm for mutual-information-based unsupervised figure-ground separation. The algorithm jointly estimates the color distributions of the foreground and background, and separates them based on their mutual information with geometric regularity. To this end, we revisit the notion of mutual information and reformulate it in terms of the photometric variable and the indicator function; and propose a sequential convex optimization strategy for solving the nonconvex optimization problem that arises. By minimizing a sequence of convex sub-problems for the mutual-information-based nonconvex energy, we efficiently attain high quality solutions for challenging unsupervised figure-ground segmentation problems. We demonstrate the capacity of our approach in numerous experiments that show convincing fully unsupervised figure-ground separation, in terms of both segmentation quality and robustness to initialization.