Generalized Range Moves
This addresses inefficiencies in iterative optimization for MRFs, which is important for computer vision and related fields, though it appears incremental as it builds on existing move-making heuristics.
The paper tackles the problem of energy minimization for multi-label Markov Random Fields (MRFs) by introducing a method that optimizes over all labels and most or all variables at once, resulting in substantial improvement over previous move-making algorithms like α-expansion and αβ-swap.
We consider move-making algorithms for energy minimization of multi-label Markov Random Fields (MRFs). Since this is not a tractable problem in general, a commonly used heuristic is to minimize over subsets of labels and variables in an iterative procedure. Such methods include α-expansion, αβ-swap, and range-moves. In each iteration, a small subset of variables are active in the optimization, which diminishes their effectiveness, and increases the required number of iterations. In this paper, we present a method in which optimization can be carried out over all labels, and most, or all variables at once. Experiments show substantial improvement with respect to previous move-making algorithms.