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MRF energy minimization and beyond via dual decomposition
| Venue: | IN: IEEE PAMI. (2011 |
| Citations: | 105 - 9 self |
BibTeX
@INPROCEEDINGS{Komodakis_mrfenergy,
author = {Nikos Komodakis and Nikos Paragios and Georgios Tziritas},
title = { MRF energy minimization and beyond via dual decomposition},
booktitle = {IN: IEEE PAMI. (2011},
year = {},
publisher = {}
}
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Abstract
This paper introduces a new rigorous theoretical framework to address discrete MRF-based optimization in computer vision. Such a framework exploits the powerful technique of Dual Decomposition. It is based on a projected subgradient scheme that attempts to solve an MRF optimization problem by first decomposing it into a set of appropriately chosen subproblems and then combining their solutions in a principled way. In order to determine the limits of this method, we analyze the conditions that these subproblems have to satisfy and we demonstrate the extreme generality and flexibility of such an approach. We thus show that, by appropriately choosing what subproblems to use, one can design novel and very powerful MRF optimization algorithms. For instance, in this manner we are able to derive algorithms that: 1) generalize and extend state-of-the-art message-passing methods, 2) optimize very tight LP-relaxations to MRF optimization, 3) and take full advantage of the special structure that may exist in particular MRFs, allowing the use of efficient inference techniques such as, e.g, graph-cut based methods. Theoretical analysis on the bounds related with the different algorithms derived from our framework and experimental results/comparisons using synthetic and real data for a variety of tasks in computer vision demonstrate the extreme potentials of our approach.
Keyphrases
dual decomposition mrf energy minimization computer vision mrf optimization extreme generality special structure real data theoretical analysis discrete mrf-based optimization extreme potential subgradient scheme full advantage principled way state-of-the-art message-passing method different algorithm particular mrfs tight lp-relaxations experimental result comparison powerful mrf optimization algorithm efficient inference technique new rigorous theoretical framework mrf optimization problem powerful technique
