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Submodular relaxation for inference in Markov random fieldsстатья
Статья опубликована в высокорейтинговом журнале
Статья опубликована в журнале из списка Web of Science и/или Scopus- Авторы: Osokin A., Vetrov D.
- Журнал: IEEE Transactions on Pattern Analysis and Machine Intelligence
- Том: 99
- Год издания: 2014
- Издательство: Institute of Electrical and Electronics Engineers
- Местоположение издательства: Piscataway, NJ, United States
- DOI: 10.1109/TPAMI.2014.2369046
- Аннотация: 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.
- Добавил в систему: Ветров Дмитрий Петрович