Evaluating group-based relevance feedback for content-based image retrieval

New relevance feedback algorithms have been developed for content-based image retrieval (CBIR) that allow the user to achieve more flexible query. In conjunction with the new user interface, called group-oriented user interface, the user's interest can be expressed with multiple groups of positive and negative image examples. This provides users with greater flexibility as compared with previous systems that consider image query as one or two-class problems. In this paper, we analyze our new algorithm qualitatively and quantitatively. For comparison with previous approaches, the systems are tested on both toy problems and real image retrieval tasks. From the results of our experiments, we suggest when and how our algorithm has advantages over the previous methods.