A Fast and Exact Energy Minimization Algorithm for Cycle MRFs


Huayan Wang, Koller Daphne ;
Proceedings of the 30th International Conference on Machine Learning, PMLR 28(3):190-198, 2013.


The presence of cycles gives rise to the difficulty in performing inference for MRFs. Handling cycles efficiently would greatly enhance our ability to tackle general MRFs. In particular, for dual decomposition of energy minimization (MAP inference), using cycle subproblems leads to a much tighter relaxation than using trees, but solving the cycle subproblems turns out to be the bottleneck. In this paper, we present a fast and exact algorithm for energy minimization in cycle MRFs, which can be used as a subroutine in tackling general MRFs. Our method builds on junction-tree message passing, with a large portion of the message entries pruned for efficiency. The pruning conditions fully exploit the structure of a cycle. Experimental results show that our algorithm is more than an order of magnitude faster than other state-of-the-art fast inference methods, and it performs consistently well in several different real problems.

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