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The \emph{maximum a posteriori} (MAP) assignment for general structure Markovrandom fields (MRFs) is computationally intractable. In this paper, we exploittree-based methods to efficiently address this problem. Our novel method, namedTree-based Iterated Local Search (T-ILS) takes advantage of the tractability oftree-structures embedded within MRFs to derive strong local search in an ILSframework. The method efficiently explores exponentially large neighborhood anddoes so with limited memory without any requirement on the cost functions. Weevaluate the T-ILS in a simulation of Ising model and two real-world problemsin computer vision: stereo matching, image denoising. Experimental resultsdemonstrate that our methods are competitive against state-of-the-art rivalswith a significant computational gain.

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