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Ranking method for optimizing precision/recall of content-based image retrieval

Zhang, Jun and Ye, Lei 2009, Ranking method for optimizing precision/recall of content-based image retrieval, in Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing in Conjunction with The UIC2009 and ATC 2009 Conferences, Institute of Electrical and Electronics Engineers, Piscataway, N. J., pp. 356-361, doi: 10.1109/UIC-ATC.2009.9.

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Title Ranking method for optimizing precision/recall of content-based image retrieval
Author(s) Zhang, Jun
Ye, Lei
Conference name Ubiquitous, Autonomic and Trusted Computing. Conference (2009 : Brisbane, Qld.)
Conference location Brisbane, Qld.
Conference dates 7-9 July 2009
Title of proceedings Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing in Conjunction with The UIC2009 and ATC 2009 Conferences
Editor(s) Werner, Bob
Publication date 2009
Conference series Ubiquitous, Autonomic and Trusted Computing Conference
Start page 356
End page 361
Total pages 6
Publisher Institute of Electrical and Electronics Engineers
Place of publication Piscataway, N. J.
Keyword(s) content-based image retrieval
performance evaluation
ranking method
Summary The ranking method is a key element of Content-based Image Retrieval (CBIR) system, which can affect the final retrieval performance. In the literature, previous ranking methods based on either distance or probability do not explicitly relate to precision and recall, which are normally used to evaluate the performance of CBIR systems. In this paper, a novel ranking method based on relative density is proposed to improve the probability based approach by ranking images in the class. The proposed method can achieve optimal precision and recall. The experiments conducted on a large photographic collection show significant improvements of retrieval performance.
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 9780769537375
Language eng
DOI 10.1109/UIC-ATC.2009.9
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 ©2009, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30039524

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.