Accelerated variational inference for Beta-Liouville mixture learning with application to 3D shapes recognition

Beta-Liouville mixture models have achieved measurable success in many computer vision and pattern recognition applications. In this paper, we develop a novel algorithm to learn this particular kind of models that have been shown to be very efficient for the clustering of proportional data. Our algorithm is based on an accelerated version of the variational Bayes approach. Experiments show that the developed algorithm work very well for the categorization of 3D shapes.