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dc.contributor.authorNikou, C.en
dc.contributor.authorLikas, A. C.en
dc.contributor.authorGalatsanos, N. P.en
dc.rightsDefault Licence-
dc.subjectbayesian modelen
dc.subjectdirichlet compound multinomial distributionen
dc.subjectgauss-markov random field prioren
dc.subjectgaussian mixtureen
dc.subjectimage segmentationen
dc.subjectspatially varying finite mixture modelen
dc.subjectgaussian scale mixturesen
dc.subjecttriplet markov-fieldsen
dc.subjectem algorithmen
dc.titleA Bayesian Framework for Image Segmentation With Spatially Varying Mixturesen
heal.type.enJournal articleen
heal.type.elΆρθρο Περιοδικούel
heal.identifier.primaryDoi 10.1109/Tip.2010.2047903-
heal.recordProviderΠανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Ηλεκτρονικών Υπολογιστών και Πληροφορικήςel
heal.abstractA new Bayesian model is proposed for image segmentation based upon Gaussian mixture models (GMM) with spatial smoothness constraints. This model exploits the Dirichlet compound multinomial (DCM) probability density to model the mixing proportions (i.e., the probabilities of class labels) and a Gauss-Markov random field (MRF) on the Dirichlet parameters to impose smoothness. The main advantages of this model are two. First, it explicitly models the mixing proportions as probability vectors and simultaneously imposes spatial smoothness. Second, it results in closed form parameter updates using a maximum a posteriori (MAP) expectation-maximization (EM) algorithm. Previous efforts on this problem used models that did not model the mixing proportions explicitly as probability vectors or could not be solved exactly requiring either time consuming Markov Chain Monte Carlo (MCMC) or inexact variational approximation methods. Numerical experiments are presented that demonstrate the superiority of the proposed model for image segmentation compared to other GMM-based approaches. The model is also successfully compared to state of the art image segmentation methods in clustering both natural images and images degraded by noise.en
heal.journalNameIeee Transactions on Image Processingen
heal.journalTypepeer reviewed-
Appears in Collections:Άρθρα σε επιστημονικά περιοδικά ( Ανοικτά)

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