GRAPH CUTS, SUM-OF-SUBMODULAR FLOW, AND LINEAR PROGRAMMING: EFFECTIVE INFERENCE IN HIGHER-ORDER MARKOV RANDOM FIELDS

Abstract

Optimization algorithms have a long history of success in computer vision, providing effective algorithms for tasks as varied as segmentation, stereo estimation, image denoising and scene understanding. A notable example of this is Graph Cuts, in which the minimum-cut problem is used to solve a class of vision problems known as first-order Markov Random Fields. Despite this success, first-order MRFs have their limitations. They cannot encode correlations between groups of pixels larger than two or easily express higher-order statistics of images. In this thesis, we generalize graph cuts to higher-order MRFs, while still maintaining the properties that make graph cuts successful. In particular, we will examine three different mathematical techniques which have combined to make previously intractable higher-order inference problems become practical within the last few years. First, order-reducing reductions, which transform higher-order problems into familiar first-order MRFs. Second, a generalization of the min-cut problem to hypergraphs, called Sum-of-Submodular optimization. And finally linear programming relaxations based on the Local Marginal Polytope, which together with Sum-of-Submodular flow results in the highly effective primal-dual algorithm SoSPD. This thesis presents all mathematical background for these algorithms, as well as an implementation and experimental comparison with state-of-the-art.