Submodular Relaxation for Inference in Markov Random Fields

Anton Osokin 1, 2 Dmitry P. Vetrov 3, 4
1 SIERRA - Statistical Machine Learning and Parsimony
DI-ENS - Département d'informatique de l'École normale supérieure, ENS Paris - École normale supérieure - Paris, Inria Paris-Rocquencourt, CNRS - Centre National de la Recherche Scientifique : UMR8548
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. [29] 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.
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IEEE Transactions on Pattern Analysis and Machine Intelligence, Institute of Electrical and Electronics Engineers, 2015, 37 (7), pp.14. 〈10.1109/TPAMI.2014.2369046〉
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Contributeur : Anton Osokin <>
Soumis le : mercredi 14 janvier 2015 - 18:07:07
Dernière modification le : vendredi 25 mai 2018 - 12:02:06

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Anton Osokin, Dmitry P. Vetrov. Submodular Relaxation for Inference in Markov Random Fields. IEEE Transactions on Pattern Analysis and Machine Intelligence, Institute of Electrical and Electronics Engineers, 2015, 37 (7), pp.14. 〈10.1109/TPAMI.2014.2369046〉. 〈hal-01103488〉

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