Ranking multimedia databases via relevance feedback with history and foresight support

Users of large multimedia databases which are common in scientific, commercial and entertainment applications frequently wish to explore databases sorted by their preferences. However, it is often difficult for users to explicitly express their preferences in a way that can be used directly in content-based retrieval systems. A promising approach to overcome the semantic gap between user preferences on the one hand and feature-based, quantitative similarity measures in multimedia databases on the other hand are relevance feedback systems that learn a ranking function through interacting with the user. In each iteration of a feedback loop, database objects marked as relevant are used to derive a new ranking function. To this end we propose to utilize not only the last iteration of relevance feedback but the history of all objects selected as relevant throughout the entire relevance feedback session. Using the same mathematical framework, we also examine reducing the iteration count in exploratory searches via taking the direction of movement through the database into account.