A robust segmentation method for the AFCM-MRF model in noisy image
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
The infinite hidden Markov random field model
IEEE Transactions on Neural Networks
An extension of the standard mixture model for image segmentation
IEEE Transactions on Neural Networks
A conditional random field approach to unsupervised texture image segmentation
EURASIP Journal on Advances in Signal Processing
Dirichlet Gaussian mixture model: Application to image segmentation
Image and Vision Computing
A possibilistic clustering approach toward generative mixture models
Pattern Recognition
A spatially-constrained normalized Gamma process prior
Expert Systems with Applications: An International Journal
Color texture segmentation based on image pixel classification
Engineering Applications of Artificial Intelligence
Pattern Recognition Letters
Margin-maximizing classification of sequential data with infinitely-long temporal dependencies
Expert Systems with Applications: An International Journal
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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.