  In the search and retrieval of multimedia objects, it is impractical to
either manually or automatically extract the contents for indexing since most
of the multimedia contents are not machine extractable, while manual extraction
tends to be highly laborious and time-consuming. However, by systematically
capturing and analyzing the feedback patterns of human users, vital information
concerning the multimedia contents can be harvested for effective indexing and
subsequent search. By learning from the human judgment and mental evaluation of
users, effective search indices can be gradually developed and built up, and
subsequently be exploited to find the most relevant multimedia objects. To
avoid hovering around a local maximum, we apply the epsilon-greedy method to
systematically explore the search space. Through such methodic exploration, we
show that the proposed approach is able to guarantee that the most relevant
objects can always be discovered, even though initially it may have been
overlooked or not regarded as relevant. The search behavior of the present
approach is quantitatively analyzed, and closed-form expressions are obtained
for the performance of two variants of the epsilon-greedy algorithm, namely
EGSE-A and EGSE-B. Simulations and experiments on real data set have been
performed which show good agreement with the theoretical findings. The present
method is able to leverage exploration in an effective way to significantly
raise the performance of multimedia information search, and enables the certain
discovery of relevant objects which may be otherwise undiscoverable.
