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Properties of series feature aggregation schemes

Zhang, Jun and Ye, Lei 2010, Properties of series feature aggregation schemes, World review of science, technology and sustainable development, vol. 7, no. 1-2, pp. 100-115, doi: 10.1504/WRSTSD.2010.032347.

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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
Journal name World review of science, technology and sustainable development
Volume number 7
Issue number 1-2
Start page 100
End page 115
Total pages 16
Publisher Inderscience Publishers
Place of publication Geneva, Switzerland
Publication date 2010-03
ISSN 1741-2242
1741-2234
Keyword(s) CBIR
content-based image retrieval
feature fusion
series feature aggregation
SFA
threshold estimation
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.
Language eng
DOI 10.1504/WRSTSD.2010.032347
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 C1.1 Refereed article in a scholarly journal
Copyright notice ©2010, Inderscience Enterprises Ltd.
Persistent URL http://hdl.handle.net/10536/DRO/DU:30039535

Document type: Journal Article
Collection: School of Information Technology
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