Region based image annotation through multiple-instance learning

Authors:
Changbo Yang;Ming Dong;Farshad Fotouhi
Affiliations:
Wayne State University, Detroit, MI;Wayne State University, Detroit, MI;Wayne State University, Detroit, MI
Venue:
Proceedings of the 13th annual ACM international conference on Multimedia
Year:
2005

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Abstract

In an annotated image database, keywords are usually associated with images instead of individual regions, which poses a major challenge for any region based image annotation algorithm. In this paper, we propose to learn the correspondence between image regions and keywords through Multiple-Instance Learning (MIL). After a representative image region has been learned for a given keyword, we consider image annotation as a problem of image classification, in which each keyword is treated as a distinct class label. The classification problem is then addressed using the Bayesian framework. The proposed image annotation method is evaluated on an image database with 5,000 images.