Segmentation of colour images using variational expectation-maximization algorithm

Abstract

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.