<XML><RECORDS><RECORD><REFERENCE_TYPE>3</REFERENCE_TYPE><REFNUM>9060</REFNUM><AUTHORS><AUTHOR>Stathopoulos,V.</AUTHOR><AUTHOR>Jose,J.M.</AUTHOR></AUTHORS><YEAR>2009</YEAR><TITLE>Bayesian Mixture Hierarchies for Automatic Image Annotation</TITLE><PLACE_PUBLISHED>European Conference on Information Retrieval</PLACE_PUBLISHED><PUBLISHER>Springer</PUBLISHER><LABEL>Stathopoulos:2009:9060</LABEL><ABSTRACT>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.</ABSTRACT></RECORD></RECORDS></XML>