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. Attached Files Name Description MIMEType Size Downloads zhang-anunified-2007.pdf Published version application/pdf 3.92MB 311 Title An unified framework based on p-norm for feature aggregation in content- based image retrieval Author(s) 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: Faculty of Science, Engineering and Built Environment School of Information Technology Open Access Collection Related Links Link Description Connect to published version (restricted access) Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. 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. Versions Version Filter Type Wed, 26 Oct 2011, 12:46:55 EST Mon, 28 Nov 2011, 11:16:13 EST Mon, 28 Nov 2011, 11:17:56 EST Fri, 16 Mar 2012, 19:07:22 EST Tue, 19 Jun 2012, 12:46:04 EST Tue, 06 Sep 2016, 17:36:45 EST Tue, 05 Dec 2017, 10:53:42 EST Fri, 07 Sep 2018, 15:33:23 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 458 Abstract Views, 311 File Downloads  -  Detailed Statistics Created: Wed, 26 Oct 2011, 12:46:55 EST