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ICPR
2004
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A Variational Approach for Color Image Segmentation

9 years 6 months ago
A Variational Approach for Color Image Segmentation
In this paper we use a variational Bayesian framework for color image segmentation. Each image is represented in the L*u*v color coordinate system before being segmented by the variational algorithm. The model chosen to describe the color images is a Gaussian mixture model. The parameter estimation uses variational learning by taking into account the uncertainty in parameter estimation. In the variational Bayesian approach we integrate over distributions of parameters. We propose a maximum log-likelihood initialization approach for the Variational Expectation-Maximization (VEM) algorithm and we apply it to color image segmentation. The segmentation task in our approach consists of the estimation of the distribution hyperparameters.
Nikolaos Nasios, Adrian G. Bors
Added 09 Nov 2009
Updated 09 Nov 2009
Type Conference
Year 2004
Where ICPR
Authors Nikolaos Nasios, Adrian G. Bors
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