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In the search and retrieval of multimedia objects, it is impractical toeither manually or automatically extract the contents for indexing since mostof the multimedia contents are not machine extractable, while manual extractiontends to be highly laborious and time-consuming. However, by systematicallycapturing and analyzing the feedback patterns of human users, vital informationconcerning the multimedia contents can be harvested for effective indexing andsubsequent search. By learning from the human judgment and mental evaluation ofusers, effective search indices can be gradually developed and built up, andsubsequently be exploited to find the most relevant multimedia objects. Toavoid hovering around a local maximum, we apply the epsilon-greedy method tosystematically explore the search space. Through such methodic exploration, weshow that the proposed approach is able to guarantee that the most relevantobjects can always be discovered, even though initially it may have beenoverlooked or not regarded as relevant. The search behavior of the presentapproach is quantitatively analyzed, and closed-form expressions are obtainedfor the performance of two variants of the epsilon-greedy algorithm, namelyEGSE-A and EGSE-B. Simulations and experiments on real data set have beenperformed which show good agreement with the theoretical findings. The presentmethod is able to leverage exploration in an effective way to significantlyraise the performance of multimedia information search, and enables the certaindiscovery of relevant objects which may be otherwise undiscoverable.

Performance Effectiveness of Multimedia Information Search Using the Epsilon-Greedy Algorithm