DocumentCode :
2175675
Title :
Medical Image Retrieval with Query-Dependent Feature Fusion Based on One-Class SVM
Author :
Huang, Yonggang ; Zhang, Jun ; Zhao, Yongwang ; Ma, Dianfu
Author_Institution :
Inst. of Adv. Comput. Technol., BeiHang Univ., Beijing, China
fYear :
2010
fDate :
11-13 Dec. 2010
Firstpage :
176
Lastpage :
183
Abstract :
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.
Keywords :
Internet; Web sites; content-based retrieval; feature extraction; image fusion; image retrieval; medical image processing; support vector machines; IRMA medical image collection; World Wide Web; automatic visual information extraction; content-based image retrieval; feature extraction; feature fusion method; image queries; medical image retrieval; one class support vector machine; one-class SVM; query dependent feature fusion; Biomedical imaging; Feature extraction; Image color analysis; Image edge detection; Image retrieval; Support vector machines; Training;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Science and Engineering (CSE), 2010 IEEE 13th International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-9591-7
Electronic_ISBN :
978-0-7695-4323-9
Type :
conf
DOI :
10.1109/CSE.2010.30
Filename :
5692472
Link To Document :
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