Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/7276
Title: A fuzzy clustering approach toward Hidden Markov random field models for enhanced spatially constrained image segmentation
Authors: Chatzis, Sotirios P. 
Varvarigou, Theodora 
Keywords: Fuzzy systems;Computing & Processing (Hardware/Software);Markov processes;Speech
Issue Date: 2008
Publisher: IEEE Xplore
Source: IEEE transactions on fuzzy systems, 2008, Volume 16, Issue 5, Pages 1351-1361
Abstract: Hidden Markov random field (HMRF) models have been widely used for image segmentation, as they appear naturally in problems where a spatially constrained clustering scheme, taking into account the mutual influences of neighboring sites, is asked for. Fuzzy c-means (FCM) clustering has also been successfully applied in several image segmentation applications. In this paper, we combine the benefits of these two approaches, by proposing a novel treatment of HMRF models, formulated on the basis of a fuzzy clustering principle. We approach the HMRF model treatment problem as an FCM-type clustering problem, effected by introducing the explicit assumptions of the HMRF model into the fuzzy clustering procedure. Our approach utilizes a fuzzy objective function regularized by Kullback--Leibler divergence information, and is facilitated by application of a mean-field-like approximation of the MRF prior. We experimentally demonstrate the superiority of the proposed approach over competing methodologies, considering a series of synthetic and real-world image segmentation applications
URI: http://ktisis.cut.ac.cy/handle/10488/7276
ISSN: 10636706
DOI: 10.1109/TFUZZ.2008.2005008
Rights: © 2008 IEEE
Type: Article
Appears in Collections:Άρθρα/Articles

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