BMVC 2004, Kingston, 7th-9th Sept, 2004
Segmentation of colour images using variational expectation-maximization algorithm
N. Nasios and A.G. Bors (University of York)
The approach proposed in this paper takes into account the uncertainty in
colour modelling by employing variational Bayesian estimation. Mixtures
of Gaussians are considered for modelling colour images. Distributions of
parameters characterising colour regions are inferred from data statistics.
The Variational Expectation-Maximization (VEM) algorithm is used for estimating
the hyperparameters corresponding to distributions of parameters. A
maximum a posteriori approach employing a dual expectation-maximization
(EM) algorithm is considered for the hyperparameter initialisation of the
VEM algorithm. In the first stage, the EM algorithm is applied on the given
colour image, while the second EM algorithm is used on distributions of parameters
resulted from several runs of the first stage EM. The VEM algorithm
is used for segmenting several colour images.