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Medical image retrieval with query-dependent feature fusion based on one-class SVM

Huang, Yonggang, Zhang, Jun, Zhao, Yongwang and Ma, Dianfu 2010, Medical image retrieval with query-dependent feature fusion based on one-class SVM, in ICCSE : 13th IEEE International Conference on Computational Science and Engineering, IEEE Computer Society Conference Publishing Services (CPS), Piscataway, N. J., pp. 176-183, doi: 10.1109/CSE.2010.30.

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Title Medical image retrieval with query-dependent feature fusion based on one-class SVM
Author(s) Huang, Yonggang
Zhang, JunORCID iD for Zhang, Jun orcid.org/0000-0002-2189-7801
Zhao, Yongwang
Ma, Dianfu
Conference name Computational Science and Engineering. Conference (13th. 2010 : Hong Kong)
Conference location Hong Kong
Conference dates 11-13 Dec. 2010
Title of proceedings ICCSE : 13th IEEE International Conference on Computational Science and Engineering
Editor(s) Kellenberger, Patrick
Publication date 2010
Conference series Computational Science and Engineering Conference
Start page 176
End page 183
Total pages 8
Publisher IEEE Computer Society Conference Publishing Services (CPS)
Place of publication Piscataway, N. J.
Summary Due to the huge growth of the World Wide Web, medical images are now available in large numbers in online repositories, and there exists the need to retrieval the images through automatically extracting visual information of the medical images, which is commonly known as content-based image retrieval (CBIR). Since each feature extracted from images just characterizes certain aspect of image content, multiple features are necessarily employed to improve the retrieval performance. Meanwhile, experiments demonstrate that a special feature is not equally important for different image queries. Most of existed feature fusion methods for image retrieval only utilize query independent feature fusion or rely on explicit user weighting. In this paper, we present a novel query dependent feature fusion method for medical image retrieval based on one class support vector machine. Having considered that a special feature is not equally important for different image queries, the proposed query dependent feature fusion method can learn different feature fusion models for different image queries only based on multiply image samples provided by the user, and the learned feature fusion models can reflect the different importances 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.
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 9781424495917
Language eng
DOI 10.1109/CSE.2010.30
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 ©2010, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30039515

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.