Fast Fusion Moves for Multi-model Estimation

We develop a fast, effective algorithm for minimizing a well-known objective function for robust multi-model estimation. Our work introduces a combinatorial step belonging to a family of powerful move-making methods like α-expansion and fusion. We also show that our subproblem can be quickly transformed into a comparatively small instance of minimum-weighted vertex-cover. In practice, these vertex-cover subproblems are almost always bipartite and can be solved exactly by specialized network flow algorithms. Experiments indicate that our approach achieves the robustness of methods like affinity propagation, whilst providing the speed of fast greedy heuristics.