A variational approach for color image segmentation

Abstract

In this paper, we use a variational Bayesian framework for color image segmentation. Each image is represented in the Luv 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.