A Bayesian Framework for Image Segmentation With Spatially Varying Mixtures
| dc.contributor.author | Nikou, C. | en |
| dc.contributor.author | Likas, A. C. | en |
| dc.contributor.author | Galatsanos, N. P. | en |
| dc.date.accessioned | 2015-11-24T17:02:21Z | |
| dc.date.available | 2015-11-24T17:02:21Z | |
| dc.identifier.issn | 1057-7149 | - |
| dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/11043 | |
| dc.rights | Default Licence | - |
| dc.subject | bayesian model | en |
| dc.subject | dirichlet compound multinomial distribution | en |
| dc.subject | gauss-markov random field prior | en |
| dc.subject | gaussian mixture | en |
| dc.subject | image segmentation | en |
| dc.subject | spatially varying finite mixture model | en |
| dc.subject | gaussian scale mixtures | en |
| dc.subject | triplet markov-fields | en |
| dc.subject | expectation-maximization | en |
| dc.subject | em algorithm | en |
| dc.subject | model | en |
| dc.subject | restoration | en |
| dc.subject | compression | en |
| dc.subject | cuts | en |
| dc.title | A Bayesian Framework for Image Segmentation With Spatially Varying Mixtures | en |
| heal.abstract | A 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.access | campus | - |
| heal.fullTextAvailability | TRUE | - |
| heal.identifier.primary | Doi 10.1109/Tip.2010.2047903 | - |
| heal.journalName | Ieee Transactions on Image Processing | en |
| heal.journalType | peer reviewed | - |
| heal.language | en | - |
| heal.publicationDate | 2010 | - |
| heal.recordProvider | Πανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Ηλεκτρονικών Υπολογιστών και Πληροφορικής | el |
| heal.type | journalArticle | - |
| heal.type.el | Άρθρο Περιοδικού | el |
| heal.type.en | Journal article | en |
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