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. Attached Files Name Description MIMEType Size Downloads zhang-rankingmethod-2009.pdf Published version application/pdf 355.01KB 252 Title Ranking method for optimizing precision/recall of content-based image retrieval Author(s) Zhang, Jun orcid.org/0000-0002-2189-7801 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 Free to Read? Yes Persistent URL http://hdl.handle.net/10536/DRO/DU:30039524 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, 12:47:04 EST Mon, 21 Nov 2011, 10:52:55 EST Mon, 21 Nov 2011, 10:54:58 EST Fri, 16 Mar 2012, 19:07:41 EST Tue, 19 Jun 2012, 12:51:58 EST Tue, 06 Sep 2016, 17:37:04 EST Sat, 17 Jun 2017, 03:08:35 EST Tue, 05 Dec 2017, 10:53:45 EST Fri, 07 Sep 2018, 15:33:28 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 2 times in Scopus Search Google Scholar Access Statistics: 639 Abstract Views, 252 File Downloads  -  Detailed Statistics Created: Wed, 26 Oct 2011, 12:47:04 EST