Bayesian Mixture Hierarchies for Automatic Image Annotation

  • Authors:
  • Vassilios Stathopoulos;Joemon M. Jose

  • Affiliations:
  • Department of Computer Science, University of Glasgow, Glasgow, UK G12 8QQ;Department of Computer Science, University of Glasgow, Glasgow, UK G12 8QQ

  • Venue:
  • ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
  • Year:
  • 2009

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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.