Bayesian mixture hierarchies for automatic image annotation

Stathopoulos, V. and Jose, J. (2009) Bayesian mixture hierarchies for automatic image annotation. In: Advances in Information Retrieval. Springer, pp. 138-149. (doi: 10.1007/978-3-642-00958-7_15)

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.

• Item Type: Book Sections
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Jose, Professor Joemon and Stathopoulos, Mr Vasileios
• Authors: Stathopoulos, V., and Jose, J.
• Subjects: Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science; Q Science > QA Mathematics > QA75 Electronic computers. Computer science
• College/School: College of Science and Engineering > School of Computing Science
• Journal Name: Lecture Notes in Computer Science
• Publisher: Springer
• ISSN: 0302-9743
• ISSN (Online): 1611-3349