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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.

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Title A new query dependent feature fusion approach for medical image retrieval based on one-class SVM
Author(s) Huang, Yonggang
Ma, Dianfu
Zhang, JunORCID iD for 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: School of Information Technology
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Created: Wed, 26 Oct 2011, 12:47:56 EST

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.