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Proceedings Paper

Neighbourhood-consensus message passing and its potentials in image processing applications
Author(s): Tijana Ružic; Aleksandra Pižurica; Wilfried Philips
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Paper Abstract

In this paper, a novel algorithm for inference in Markov Random Fields (MRFs) is presented. Its goal is to find approximate maximum a posteriori estimates in a simple manner by combining neighbourhood influence of iterated conditional modes (ICM) and message passing of loopy belief propagation (LBP). We call the proposed method neighbourhood-consensus message passing because a single joint message is sent from the specified neighbourhood to the central node. The message, as a function of beliefs, represents the agreement of all nodes within the neighbourhood regarding the labels of the central node. This way we are able to overcome the disadvantages of reference algorithms, ICM and LBP. On one hand, more information is propagated in comparison with ICM, while on the other hand, the huge amount of pairwise interactions is avoided in comparison with LBP by working with neighbourhoods. The idea is related to the previously developed iterated conditional expectations algorithm. Here we revisit it and redefine it in a message passing framework in a more general form. The results on three different benchmarks demonstrate that the proposed technique can perform well both for binary and multi-label MRFs without any limitations on the model definition. Furthermore, it manifests improved performance over related techniques either in terms of quality and/or speed.

Paper Details

Date Published: 3 February 2011
PDF: 7 pages
Proc. SPIE 7870, Image Processing: Algorithms and Systems IX, 78700Z (3 February 2011); doi: 10.1117/12.872464
Show Author Affiliations
Tijana Ružic, Ghent Univ. (Belgium)
Aleksandra Pižurica, Ghent Univ. (Belgium)
Wilfried Philips, Ghent Univ. (Belgium)

Published in SPIE Proceedings Vol. 7870:
Image Processing: Algorithms and Systems IX
Jaakko T. Astola; Karen O. Egiazarian, Editor(s)

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