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Tree-based iterated local search for Markov random fields with applications in image analysis

Tran,T, Phung,D and Venkatesh,S 2014, Tree-based iterated local search for Markov random fields with applications in image analysis, Journal of heuristics, vol. 21, no. 1, pp. 25-45, doi: 10.1007/s10732-014-9270-1.

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Title Tree-based iterated local search for Markov random fields with applications in image analysis
Author(s) Tran,TORCID iD for Tran,T orcid.org/0000-0001-6531-8907
Phung,DORCID iD for Phung,D orcid.org/0000-0002-9977-8247
Venkatesh,SORCID iD for Venkatesh,S orcid.org/0000-0001-8675-6631
Journal name Journal of heuristics
Volume number 21
Issue number 1
Start page 25
End page 45
Total pages 21
Publisher Springer
Place of publication Berlin, Germany
Publication date 2014
ISSN 1381-1231
1572-9397
Keyword(s) Belief propagation
Iterated local search
MAP assignment
Markov random fields
Strong local search
Science & Technology
Technology
Computer Science, Artificial Intelligence
Computer Science, Theory & Methods
Computer Science
BELIEF-PROPAGATION
ENERGY MINIMIZATION
STATISTICAL-ANALYSIS
GENETIC ALGORITHM
MAP ESTIMATION
GRAPH CUTS
SEGMENTATION
OPTIMIZATION
MRF
DISTRIBUTIONS
Summary 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.
Language eng
DOI 10.1007/s10732-014-9270-1
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2014, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30073006

Document type: Journal Article
Collection: Centre for Pattern Recognition and Data Analytics
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