Fast Postprocessing for Difficult Discrete Energy Minimization Problems

Despite the rapid progress in discrete energy minimization, certain problems involving high connectivity and a high number of labels are considered very hard but are still very relevant in computer vision. We propose a postprocessing technique to improve the sub-optimal results of the existing methods on such problems. Our core contribution is a mapping between the binary min-cut problem and finding the shortest path in a directed acyclic graph. Using this mapping, we present an algorithm to find an approximate solution for the min-cut problem. We also extend the same idea for multi-label factor-graphs in the form of an iterative move-making algorithm. The proposed algorithm is extremely fast, yet outperforms the existing techniques in terms of accuracy as well as the computational time. We demonstrate competitive or better results on problems where already high-quality work is done.