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

Version 2 2024-06-04, 11:43
Version 1 2015-05-06, 15:42
journal contribution
posted on 2024-06-04, 11:43 authored by Truyen TranTruyen Tran, D Phung, Svetha VenkateshSvetha Venkatesh
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.

History

Journal

Journal of heuristics

Volume

21

Pagination

25-45

Location

Berlin, Germany

ISSN

1381-1231

eISSN

1572-9397

Language

eng

Publication classification

C Journal article, C1 Refereed article in a scholarly journal

Copyright notice

2014, Springer

Issue

1

Publisher

Springer