A reinforcement learning approach to query-less image retrieval

Abstract

Search algorithms in image retrieval tend to focus exclusively on giving the user more and more similar images based on queries that the user has to explicitly formulate. Implicitly, such systems limit the users exploration of the image space and thus remove the potential for serendipity. Thus, in recent years there has been an increased interest in developing exploration–exploitation algorithms for image search. We present an interactive image retrieval system that combines Reinforcement Learning together with a user interface designed to allow users to actively engage in directing the search. Reinforcement Learning is used to model the user interests by allowing the system to trade off between exploration (unseen types of image) and exploitation (images the system thinks are relevant).