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Properties of series feature aggregation schemes

Zhang, Jun and Ye, Lei 2008, Properties of series feature aggregation schemes, in ICITA 2008 : Fifth international conference on information technology &​ applications, ICITA, Bathurst, N.S.W., pp. 361-364.

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Title Properties of series feature aggregation schemes
Author(s) Zhang, JunORCID iD for Zhang, Jun orcid.org/0000-0002-2189-7801
Ye, Lei
Conference name International Conference on Information Technology and Applications (5th : 2008 : Cairns, Queensland.)
Conference location Cairns, Qld.
Conference dates 23-26 June 2008
Title of proceedings ICITA 2008 : Fifth international conference on information technology &​ applications
Editor(s) Tien, David
Kavakli, Manolya
Publication date 2008
Conference series International Conference on Information Technology and Applications
Start page 361
End page 364
Total pages 4
Publisher ICITA
Place of publication Bathurst, N.S.W.
Summary Feature aggregation is a critical technique in content-based image retrieval (CBIR) that combines multiple feature distances to obtain image dissimilarity. Conventional parallel feature aggregation (PFA) schemes failed to effectively filter out the irrelevant images using individual visual features before ranking images in collection. Series feature aggregation (SFA) is a new scheme that aims to address this problem. This paper investigates three important properties of SFA that are significant for design of systems. They reveal the irrelevance of feature order and the convertibility of SFA and PFA as well as the superior performance of SFA. Furthermore, based on Gaussian kernel density estimator, the authors propose a new method to estimate the visual threshold, which is the key parameter of SFA. Experiments, conducted with IAPR TC-12 benchmark image collection (ImageCLEF2006) that contains over 20,000 photographic images and defined queries, have shown that SFA can outperform conventional PFA schemes.
ISBN 9780980326727
0980326729
Language eng
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 ©2008, ICITA
Persistent URL http://hdl.handle.net/10536/DRO/DU:30039520

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.