Journal of Heuristics

, Volume 21, Issue 1, pp 25–45 | Cite as

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



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.


Iterated local search Strong local search Belief propagation Markov random fields MAP assignment 


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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Department of ComputingCurtin UniversityBentleyAustralia
  2. 2.Center for Pattern Recognition and Data AnalyticsDeakin UniversityWaurn PondsAustralia

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