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Series feature aggregation for content-based image retrieval

Zhang, Jun and Ye, Lei 2007, Series feature aggregation for content-based image retrieval, in ICSPCS 2007 : Proceedings of the International Conference on Signal Processing and Communication Systems, [DSP for Communication Systems], Tarrawanna, N.S.W., pp. 96-101.

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Title Series feature aggregation for content-based image retrieval
Author(s) Zhang, JunORCID iD for Zhang, Jun orcid.org/0000-0002-2189-7801
Ye, Lei
Conference name International Conference on Signal Processing and Communication Systems (1st : 2007 : Gold Coast, Qld.)
Conference location Gold Coast, Qld.
Conference dates 17-19 Dec. 2007
Title of proceedings ICSPCS 2007 : Proceedings of the International Conference on Signal Processing and Communication Systems
Editor(s) Wysocki, Beata J.
Wysocki, Tadeusz A.
Publication date 2007
Conference series International Conference on Signal Processing and Communication Systems
Start page 96
End page 101
Publisher [DSP for Communication Systems]
Place of publication Tarrawanna, N.S.W.
Summary Feature aggregation is a critical technique in content-based image retrieval systems that employ multiple visual features to characterize image content. One problem in feature aggregation is that image similarity in different feature spaces can not be directly comparable with each other. To address this problem, a new feature aggregation approach, series feature aggregation (SFA), is proposed in this paper. In contrast to merging incomparable feature distances in different feature spaces to get aggregated image similarity in the conventional feature aggregation approach, the series feature aggregation directly deal with images in each feature space to avoid comparing different feature distances. SFA is effectively filtering out irrelevant images using individual features in each stage and the remaining images are images that collectively described by all features. 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 the parallel feature aggregation and linear distance combination schemes. Furthermore, SFA is able to retrieve more relevant images in top ranked outputs that brings better user experience in finding more relevant images quickly.
ISBN 0975693433
9780975693438
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, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30039518

Document type: Conference Paper
Collection: School of Information Technology
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