Mixture of Gaussians (MoG) model is a useful tool in statistical learning. In many learning processes that are based on mixture models, computational requirements are very demanding due to the large number of components involved in the model. We propose a novel algorithm for learning a simplified representation of a Gaussian mixture, that is based on the Unscented Transform which was introduced for filtering nonlinear dynamical systems. The superiority of the proposed method is validated on both simulation experiments and categorization of a real image database. The proposed categorization methodology is based on modeling each image using a Gaussian mixture model. A category model is obtained by learning a simplified mixture model from all the images in the category.

This work focuses on a general framework for image categorization, classification and retrieval that may be appropriate for medical image archives. The proposed methodology is comprised of a continuous and probabilistic image representation scheme using Gaussian mixture modeling (MoG) along with information-theoretic image matching measures (KL). A category model is obtained by learning a reduced model from all the images in the category. We propose a novel algorithm for learning a reduced representation of a MoG, that is based on the unscented-transform. The superiority of the proposed method is validated on both simulation experiments and categorization of a real medical image database.