region based image annotation through multiple-instance learning
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.
region image annotation image image multiple-instance learning learning image database association challenge algorithm paper image classification class classification label method  framework  automatic image annotation imaging