Image modeling and segmentation using incremental Bayesian mixture models

Authors:
Constantinos Constantinopoulos;Aristidis Likas
Affiliations:
Department of Computer Science, University of Ioannina, Ioannina, Greece;Department of Computer Science, University of Ioannina, Ioannina, Greece
Venue:
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
Year:
2007

Citing 3
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Abstract

Many image modeling and segmentation problems have been tackled using Gaussian MixtureModels (GMM). The two most important issues in image modeling using GMMs is the selection of the appropriate low level features and the specification of the appropriate number of GMM components. In this work we deal with the second issue and present an approach for GMM-based image modeling employing an incremental variational algorithm for Bayesian GMM training that automatically specifies the number of mixture components. Experimental results on natural and texture images indicate that themethod yields reasonable models without requiring the a priori specification of the number of components.