Submodular Relaxation for Inference in Markov Random Fields.
IEEE Trans Pattern Anal Mach Intell
; 37(7): 1347-59, 2015 Jul.
Article
en En
| MEDLINE
| ID: mdl-26352444
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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01-internacional
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MEDLINE
Tipo de estudio:
Clinical_trials
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Health_economic_evaluation
Idioma:
En
Revista:
IEEE Trans Pattern Anal Mach Intell
Asunto de la revista:
INFORMATICA MEDICA
Año:
2015
Tipo del documento:
Article
Pais de publicación:
Estados Unidos