Submodular Decomposition Framework for Inference in Associative Markov Networks with Global Constraints
In the paper we address the problem of finding the most probable state of discrete Markov random field (MRF) with associative pairwise terms. Although of practical importance, this problem is known to be NP-hard in general. We propose a new type of MRF decomposition, submodular decomposition (SMD). Unlike existing decomposition approaches SMD decomposes the initial problem into subproblems corresponding to a specific class label while preserving the graph structure of each subproblem. Such decomposition enables us to take into account several types of global constraints in an efficient manner. We study theoretical properties of the proposed approach and demonstrate its applicability on a number of problems.
- Pub Date:
- March 2011
- Computer Science - Computer Vision and Pattern Recognition;
- Computer Science - Discrete Mathematics;
- Mathematics - Optimization and Control
- 17 pages. Shorter version to appear in CVPR 2011