Evidence combination for multi-point query learning in content-based image retrieval

Urban, J. and Jose, J.M. (2004) Evidence combination for multi-point query learning in content-based image retrieval. In: IEEE Sixth International Symposium on Multimedia Software Engineering, Miami, Florida, 13-15 December 2004, pp. 583-586. ISBN 9780769522173 (doi: 10.1109/MMSE.2004.44)

Abstract

In multipoint query learning a number of query representatives are selected based on the positive feedback samples. The similarity score to a multipoint 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 multipoint queries relies heavily on the right choice of settings for the fusion. Unlike previous results, suggesting that multipoint 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.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Jose, Professor Joemon and Urban, Jana
• Authors: Urban, J., and Jose, J.M.
• Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
• College/School: College of Science and Engineering > School of Computing Science
• Publisher: IEEE Computer Society
• ISBN: 9780769522173
• Copyright Holders: Copyright © 2004 IEEE Computer Society
• First Published: First published in Proceedings of the 6th IEEE International Symposium on Multimedia Software Engineering
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher