Spatial image segmentation based on Beta-Liouville mixture models and Markov Random Field

Finite mixture models are one of the most widely used probabilistic methods for image segmentation. In this paper, we propose and investigate a mixture model based on Beta-Liouville distributions, which offers more flexibility than previously proposed models. The proposed approach is based on integration of mixture models with Markov Random Field (MRF) with a novel factor that is induced to reduce noise and illumination in images. The model is learned using Expectation Maximization (EM) algorithm based on Newton-Raphson approach. The proposed approach is compared with mixtures of Gaussian, Dirichlet and generalized Dirichlet distributions with integrated MRF. The experimental results demonstrate that proposed segmentation framework gives better performance and better results as compared to mixtures of Gaussian, Dirichlet and generalized Dirichlet with MRF.