Evidence Combination for Multi-Point Query Learning in Content-Based Image Retrieval

Authors:
Jana Urban;Joemon M. Jose
Affiliations:
University of Glasgow;University of Glasgow
Venue:
ISMSE '04 Proceedings of the IEEE Sixth International Symposium on Multimedia Software Engineering
Year:
2004

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Abstract

In Multi-Point Query Learning a number of query representatives are selected based on the positive feedback samples. The similarity score to a multi-point query is obtained from merging the individual scores. In this paper, we investigate three different combination strategies and present a comparative evaluation of their performance. Results show that the performance of multi-point queries relies heavily on the right choice of settings for the fusion. Unlike previous results, suggesting that multi-point queries generally perform better than a single query representation, our evaluation results do not allow such an overall conclusion. Instead our study points to the type of queries for which query expansion is better suited than a single query, and vice versa.