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A Fuzzy Clustering Approach Toward Hidden Markov Random Field Models for Enhanced Spatially Constrained Image Segmentation

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2 Author(s)
Sotirios P. Chatzis ; Nat. Tech. Univ. of Athens, Athens ; Theodora A. Varvarigou

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.

Published in:

IEEE Transactions on Fuzzy Systems  (Volume:16 ,  Issue: 5 )