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While Markov Random Fields (MRFs) are widely used in computer vision, theypresent a quite challenging inference problem. MRF inference can be acceleratedby pre-processing techniques like Dead End Elimination (DEE) or QPBO-basedapproaches which compute the optimal labeling of a subset of variables. Thesetechniques are guaranteed to never wrongly label a variable but they oftenleave a large number of variables unlabeled. We address this shortcoming byinterpreting pre-processing as a classification problem, which allows us totrade off false positives (i.e., giving a variable an incorrect label) versusfalse negatives (i.e., failing to label a variable). We describe an efficientdiscriminative rule that finds optimal solutions for a subset of variables. Ourtechnique provides both per-instance and worst-case guarantees concerning thequality of the solution. Empirical studies were conducted over severalbenchmark datasets. We obtain a speedup factor of 2 to 12 over expansion moveswithout preprocessing, and on difficult non-submodular energy functions produceslightly lower energy.

A discriminative view of MRF pre-processing algorithms