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.
The research leading to this paper was supported by European Commission under contracts FP6-027026(K-Space) and FP6-027122(Salero).
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Stathopoulos, V., Jose, J.M. (2009). Bayesian Mixture Hierarchies for Automatic Image Annotation. In: Boughanem, M., Berrut, C., Mothe, J., Soule-Dupuy, C. (eds) Advances in Information Retrieval. ECIR 2009. Lecture Notes in Computer Science, vol 5478. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00958-7_15
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DOI: https://doi.org/10.1007/978-3-642-00958-7_15
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