Bayesian Mixture Hierarchies for Automatic Image Annotation

Previous research on automatic image annotation has shown that accurate estimates of the class conditional densities in generative models have a positive effect in annotation performance. We focus on the problem of density estimation in the context of automatic image annotation and propose a novel Bayesian hierarchical method for estimating mixture models of Gaussian components. The proposed methodology is examined in a well-known benchmark image collection and the results demonstrate its competitiveness with the state of the art.