Balancing Exploration - Exploitation in Image Retrieval

Dorota Glowacka, Sayantan Hore

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

In recent years there has been an increased interest in developing exploration– exploitation algorithms for image search. However, little research has been done as to what type of image search such techniques might be most beneficial. We present an interactive image retrieval system that combines Reinforcement Learning with an 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). A task-based user study indicates that for certain types of searches a traditional exploitation-based system is more than adequate, while for others a more complex system trading off exploration and exploitation is more beneficial.
Original languageEnglish
Title of host publicationPosters, Demos, Late-breaking Results and Workshop Proceedings of the 22nd Conference on User Modeling, Adaptation, and Personalization co-located with the 22nd Conference on User Modeling, Adaptation, and Personalization (UMAP2014), Aalborg, Denmark, July 7-11, 2014.
Number of pages4
Publication statusPublished - 2014

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