Active browsing using similarity pyramids

In this paper, we describe a new approach to managing large image databases which we call active browsing. Active browsing integrates relevance feedback into the browsing environment, so that users can modify the database’s organization to suit the desired task. Our method is based on a similarity pyramid data structure which hierarchically organizes the database so that it can be efficiently browsed. At coarse levels, the similarity pyramid allows users to view the database as large clusters of similar images. Alternatively, users can “zoom into” finer levels to view individual images. We discuss relevance feedback for the browsing process, and argue that it is fundamentally different from relevance feedback for more traditional search-by-query tasks. We propose two fundamental operations for active browsing: pruning and reorganization. Both of these operation depend on a user defined relevance set which represents the image or set of images desired by the user. We present statistical methods for accurately pruning the database, and we propose a new “worm hole” distance metric for reorganizing the database so that members of the relevance set are grouped together.