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.

Attached Files
Name Description MIMEType Size Downloads

Title Tree-based iterated local search for Markov random fields with applications in image analysis
Author(s) Tran,TORCID iD for Tran,T
Phung,DORCID iD for Phung,D
Venkatesh,SORCID iD for Venkatesh,S
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
Keyword(s) Belief propagation
Iterated local search
MAP assignment
Markov random fields
Strong local search
Science & Technology
Computer Science, Artificial Intelligence
Computer Science, Theory & Methods
Computer Science
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

Connect to link resolver
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 2 times in TR Web of Science
Scopus Citation Count Cited 2 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 730 Abstract Views, 2 File Downloads  -  Detailed Statistics
Created: Wed, 06 May 2015, 15:42:25 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact