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

TL;DR

This paper introduces an epsilon-greedy algorithm-based method for multimedia information search that systematically explores and learns from user feedback to improve retrieval relevance, demonstrating both theoretical guarantees and empirical success.

Contribution

It applies the epsilon-greedy algorithm to multimedia search, providing a novel exploration strategy that guarantees discovery of relevant objects and analyzing its performance theoretically and experimentally.

Findings

The epsilon-greedy approach guarantees finding the most relevant multimedia objects.

Theoretical analysis provides closed-form performance expressions for two algorithm variants.

Experiments confirm the method's effectiveness in real data scenarios.

Abstract

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.