Aggregative query generation

Ren, R., Halvey, M. and Jose, J. (2009) Aggregative query generation. In: IEEE International Conference on Multimedia and Expo, 2009. ICME 2009, New York, NY , U.S.A., 28 Jun - 3 Jul 2009, pp. 850-853. ISBN 9781424442904 (doi: 10.1109/ICME.2009.5202628)

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Publisher's URL: http://dx.doi.org/10.1109/ICME.2009.5202628

Abstract

This paper proposes an aggregative query generation which exploits a media document representation called feature term to create a query from multiple media examples, e.g. images. A feature term denotes an interval of one media feature dimension, such as a bin in colour histogram. This approach (1) can easily accumulate features from multiple query examples to generate an efficient query; (2) enables the exploration of text-based retrieval models for multimedia retrieval. Two criteria, minimised chi2 and maximised entropy, are proposed to optimise feature term selection. Two ranking functions, KL divergence and tf-idf based BM25 model, are used for relevance estimation. Experiments on the Corel photo collection demonstrate the effectiveness of feature terms.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Jose, Professor Joemon and Ren, Dr R and Halvey, Dr Martin
Authors: Ren, R., Halvey, M., and Jose, J.
College/School:College of Science and Engineering > School of Computing Science
ISBN:9781424442904

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