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An unified framework based on p-norm for feature aggregation in content- based image retrieval

Zhang, Jun and Ye, Lei 2007, An unified framework based on p-norm for feature aggregation in content- based image retrieval, in ISM 2007 : Proceedings of the Ninth IEEE International Symposium on Multimedia, IEEE, Piscataway, N. J., pp. 195-201, doi: 10.1109/ISM.2007.4412374.

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Title An unified framework based on p-norm for feature aggregation in content- based image retrieval
Author(s) Zhang, JunORCID iD for Zhang, Jun orcid.org/0000-0002-2189-7801
Ye, Lei
Conference name International Symposium on Multimedia (9th : 2007 : Taichung, Taiwan)
Conference location Taichung, Taiwan
Conference dates 10-12 Dec. 2007
Title of proceedings ISM 2007 : Proceedings of the Ninth IEEE International Symposium on Multimedia
Editor(s) Kellenberger, Patrick
Publication date 2007
Conference series International Symposium on Multimedia
Start page 195
End page 201
Publisher IEEE
Place of publication Piscataway, N. J.
Summary Feature aggregation is a critical technique in content- based image retrieval systems that employ multiple visual features to characterize image content. In this paper, the p-norm is introduced to feature aggregation that provides a framework to unify various previous feature aggregation schemes such as linear combination, Euclidean distance, Boolean logic and decision fusion schemes in which previous schemes are instances. Some insights of the mechanism of how various aggregation schemes work are discussed through the effects of model parameters in the unified framework. Experiments show that performances vary over feature aggregation schemes that necessitates an unified framework in order to optimize the retrieval performance according to individual queries and user query concept. Revealing experimental results conducted with IAPR TC-12 ImageCLEF2006 benchmark collection that contains over 20,000 photographic images are presented and discussed.
Notes This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
ISBN 9780769530581
0769530583
Language eng
DOI 10.1109/ISM.2007.4412374
Field of Research 080704 Information Retrieval and Web Search
080109 Pattern Recognition and Data Mining
Socio Economic Objective 890301 Electronic Information Storage and Retrieval Services
HERDC Research category E1.1 Full written paper - refereed
Copyright notice ©2007, IEEE
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30039519

Document type: Conference Paper
Collections: School of Information Technology
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.