A discriminative view of MRF pre-processing algorithms

TL;DR

This paper introduces a discriminative approach to MRF pre-processing that improves inference speed and solution quality by balancing false positives and negatives, with empirical validation on benchmark datasets.

Contribution

It proposes a novel discriminative rule for MRF pre-processing that offers guarantees and enhances inference efficiency and accuracy.

Findings

Speedup factor of 2 to 12 over expansion moves without preprocessing

Produces slightly lower energy on difficult non-submodular functions

Provides both per-instance and worst-case solution guarantees

Abstract

While Markov Random Fields (MRFs) are widely used in computer vision, they present a quite challenging inference problem. MRF inference can be accelerated by pre-processing techniques like Dead End Elimination (DEE) or QPBO-based approaches which compute the optimal labeling of a subset of variables. These techniques are guaranteed to never wrongly label a variable but they often leave a large number of variables unlabeled. We address this shortcoming by interpreting pre-processing as a classification problem, which allows us to trade off false positives (i.e., giving a variable an incorrect label) versus false negatives (i.e., failing to label a variable). We describe an efficient discriminative rule that finds optimal solutions for a subset of variables. Our technique provides both per-instance and worst-case guarantees concerning the quality of the solution. Empirical studies were conducted over several benchmark datasets. We obtain a speedup factor of 2 to 12 over expansion moves without preprocessing, and on difficult non-submodular energy functions produce slightly lower energy.