A new query dependent feature fusion approach for medical image retrieval based on one-class SVM Huang, Yonggang, Ma, Dianfu, Zhang, Jun, Zhao, Yongwang and Yi, Shengwei 2011, A new query dependent feature fusion approach for medical image retrieval based on one-class SVM, Journal of computational information systems, vol. 7, no. 3, pp. 654-665. Attached Files Name Description MIMEType Size Downloads zhang-anewquery-2011.pdf Published version application/pdf 265.06KB 163 Title A new query dependent feature fusion approach for medical image retrieval based on one-class SVM Author(s) Huang, Yonggang Ma, Dianfu Zhang, Jun orcid.org/0000-0002-2189-7801 Zhao, Yongwang Yi, Shengwei Journal name Journal of computational information systems Volume number 7 Issue number 3 Start page 654 End page 665 Total pages 12 Publisher Binary Information Press Place of publication Bethel, Conn. Publication date 2011-03 ISSN 1553-9105 Keyword(s) medical image retrieval query dependent feature fusion one class SVM CBIR Summary With the development of the internet, medical images are now available in large numbers in online repositories, and there exists the need to retrieval the medical images in the content-based ways through automatically extracting visual information of the medical images. Since a single feature extracted from images just characterizes certain aspect of image content, multiple features are necessarily employed to improve the retrieval performance. Furthermore, a special feature is not equally important for different image queries since a special feature has different importance in reflecting the content of different images. However, most existed feature fusion methods for image retrieval only utilize query independent feature fusion or rely on explicit user weighting. In this paper, based on multiply query samples provided by the user, we present a novel query dependent feature fusion method for medical image retrieval based on one class support vector machine. The proposed query dependent feature fusion method for medical image retrieval can learn different feature fusion models for different image queries, and the learned feature fusion models can reflect the different importance of a special feature for different image queries. The experimental results on the IRMA medical image collection demonstrate that the proposed method can improve the retrieval performance effectively and can outperform existed feature fusion methods for image retrieval. 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 C1.1 Refereed article in a scholarly journal Copyright notice ©2011, Binary Information Press Free to Read? Yes Persistent URL http://hdl.handle.net/10536/DRO/DU:30039528 Document type: Journal Article Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Open Access Collection Related Links Link Description Connect to published version 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, 12:47:56 EST Fri, 11 May 2012, 14:16:03 EST Fri, 11 May 2012, 14:22:14 EST Thu, 05 Jul 2012, 11:42:37 EST Thu, 11 Dec 2014, 17:25:37 EST Tue, 06 Sep 2016, 17:37:16 EST Tue, 05 Dec 2017, 10:53:45 EST Fri, 07 Sep 2018, 15:33:30 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 4 times in Scopus Search Google Scholar Access Statistics: 432 Abstract Views, 163 File Downloads  -  Detailed Statistics Created: Wed, 26 Oct 2011, 12:47:56 EST