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Hidden fuzzy Markov chain model with K discrete classes

Abstract : This paper deals with a new unsupervised fuzzy Bayesian segmentation method based on the hidden Markov chain model, in order to separate continuous from discrete components in the hidden data. We present a new F-HMC (fuzzy hidden Markov chain) related to three hard classes, based on a general extension of the previously algorithms proposed. For a given observation, the hidden variable owns a density according to a measure containing Dirac and Lebesgue components. We have performed our approach in the multispectral context. The hyper-parameters are estimated using a Stochastic Expectation Maximization (SEM) algorithm. We present synthetic simulations and also segmentation results related to real multi-band data.
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Contributor : Ahmed Gamal Eldin Connect in order to contact the contributor
Submitted on : Monday, August 22, 2011 - 12:57:59 PM
Last modification on : Thursday, August 4, 2022 - 4:52:34 PM
Long-term archiving on: : Friday, November 25, 2011 - 11:51:01 AM


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  • HAL Id : hal-00616372, version 1



Ahmed Gamal Eldin, Fabien Salzenstein, Christophe Collet. Hidden fuzzy Markov chain model with K discrete classes. Information Sciences Signal Processing and their Applications (ISSPA), May 2010, Kuala Lumpur, Malaysia. ⟨hal-00616372⟩



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