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|Title:||A fuzzy clustering approach toward Hidden Markov random field models for enhanced spatially constrained image segmentation||Authors:||Chatzis, Sotirios P.
|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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