FeedbackBypass: A New Approach to Interactive Similarity Query Processing

Abstract In recent years, several methods have been proposed for implementing interactive similarity queries on multimedia databases. Common to all these methods is the idea to exploit user feedback in order to progressively adjust the query parameters and to eventually converge to an “optimal” parameter setting. However, all these methods also share the feature to “forget” user preferences across multiple query sessions, thus requiring the feedback loop to be restarted for every new query (i.e. using default parameter values), which not only is frustrating from the user’s point of view but is also a significant waste of system resources. In this paper we present FeedbackBypass, a new approach to interactive similarity query processing. It complements the role of relevance feedback engines by storing and maintaining the query parameters, determined with feedback loops over time, using a wavelet-based data structure called “Simplex Tree”. For each query, a favorable set of query parameters can be determined using the Simplex Tree. This information can then be used to either “bypass” the feedback loop completely for already-seen queries, or to start the search process from a near-optimal configuration. FeedbackBypass can be well combined with all state-of-the-art relevance feedback techniques working in high-dimensional vector spaces. Its storage requirements scale linearly with the dimensionality of the query space, making even sophisticated query spaces amenable. Experimental results demonstrate both the effectivness and efficiency of our technique.