Tree-based iterated local search for Markov random fields with applications in image analysis
dc.contributor.author | Tran, The Truyen | |
dc.contributor.author | Phung, D. | |
dc.contributor.author | Venkatesh, S. | |
dc.date.accessioned | 2017-01-30T14:32:36Z | |
dc.date.available | 2017-01-30T14:32:36Z | |
dc.date.created | 2015-10-29T04:09:59Z | |
dc.date.issued | 2014 | |
dc.identifier.citation | Tran, T.T. and Phung, D. and Venkatesh, S. 2014. Tree-based iterated local search for Markov random fields with applications in image analysis. Journal of Heuristics. 21 (1): pp. 25-45. | |
dc.identifier.uri | http://hdl.handle.net/20.500.11937/39305 | |
dc.identifier.doi | 10.1007/s10732-014-9270-1 | |
dc.description.abstract |
The maximum a posteriori assignment for general structure Markov random fields is computationally intractable. In this paper, we exploit tree-based methods to efficiently address this problem. Our novel method, named Tree-based Iterated Local Search (T-ILS), takes advantage of the tractability of tree-structures embedded within MRFs to derive strong local search in an ILS framework. The method efficiently explores exponentially large neighborhoods using a limited memory without any requirement on the cost functions. We evaluate the T-ILS on a simulated Ising model and two real-world vision problems: stereo matching and image denoising. Experimental results demonstrate that our methods are competitive against state-of-the-art rivals with significant computational gain. | |
dc.publisher | Kluwer Academic Publishers | |
dc.title | Tree-based iterated local search for Markov random fields with applications in image analysis | |
dc.type | Journal Article | |
dcterms.source.volume | 21 | |
dcterms.source.number | 1 | |
dcterms.source.startPage | 25 | |
dcterms.source.endPage | 45 | |
dcterms.source.issn | 1381-1231 | |
dcterms.source.title | Journal of Heuristics | |
curtin.department | Multi-Sensor Proc & Content Analysis Institute | |
curtin.accessStatus | Fulltext not available |
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