Submodular relaxation for inference in Markov random fields
Abstract
In this paper we address the problem of finding the most probable state of a discrete Markov random field (MRF), also known as the MRF energy minimization problem. The task is known to be NP-hard in general and its practical importance motivates numerous approximate algorithms. We propose a submodular relaxation approach (SMR) based on a Lagrangian relaxation of the initial problem. Unlike the dual decomposition approach of Komodakis et al., 2011 SMR does not decompose the graph structure of the initial problem but constructs a submodular energy that is minimized within the Lagrangian relaxation. Our approach is applicable to both pairwise and high-order MRFs and allows to take into account global potentials of certain types. We study theoretical properties of the proposed approach and evaluate it experimentally.
Keywords
Cite
@article{arxiv.1501.03771,
title = {Submodular relaxation for inference in Markov random fields},
author = {Anton Osokin and Dmitry Vetrov},
journal= {arXiv preprint arXiv:1501.03771},
year = {2015}
}
Comments
This paper is accepted for publication in IEEE Transactions on Pattern Analysis and Machine Intelligence