TY - GEN
T1 - Medical image retrieval with query-dependent feature fusion based on one-class SVM
AU - Huang, Yonggang
AU - Zhang, Jun
AU - Zhao, Yongwang
AU - Ma, Dianfu
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=79951605330&partnerID=8YFLogxK
U2 - 10.1109/CSE.2010.30
DO - 10.1109/CSE.2010.30
M3 - Conference contribution
AN - SCOPUS:79951605330
SN - 9780769543239
T3 - Proceedings - 2010 13th IEEE International Conference on Computational Science and Engineering, CSE 2010
SP - 176
EP - 183
BT - Proceedings - 2010 13th IEEE International Conference on Computational Science and Engineering, CSE 2010
T2 - 2010 13th IEEE International Conference on Computational Science and Engineering, CSE 2010
Y2 - 11 December 2010 through 13 December 2010
ER -