Variational segmentation of color images

A variational Bayesian framework is employed in the paper for image segmentation using color clustering. A Gaussian mixture model is used to represent color distributions. Variational expectation-maximization (VEM) algorithm takes into account the uncertainty in the parameter estimation ensuring a lower bound on the approximation error. In the variational Bayesian approach we integrate over distributions of parameters. The processing task in this case consists of estimating the hyperparameters of these distributions. We propose a maximum log-likelihood initialization approach for the Variational Expectation-Maximization (VEM) algorithm. The proposed algorithm is applied to image segmentation using color clustering when representing the images in the L*u*v color coordinate system.

• Type: Conference
• Year: 2005
• Where: ICIP
• Authors: Nikolaos Nasios, Adrian G. Bors
• Keywords: ICIP 2005, Image Processing, Variational Segmentation