DocumentCode :
3209105
Title :
Orthogonal complement component analysis for positive samples in SVM based relevance feedback image retrieval
Author :
Tao, Dacheng ; Tang, Xiaoou
Author_Institution :
Dept. of Inf. Eng., Chinese Univ. of Hong Kong, China
Volume :
2
fYear :
2004
fDate :
27 June-2 July 2004
Abstract :
Relevance feedback (RF) is an important tool to improve the performance of content-based image retrieval system. Support vector machine (SVM) based RF is popular because it can generalize better than most other classifiers. However, directly using SVM in RF may not be appropriate, since SVM treats the positive and negative feedbacks equally. Given the different properties of positive samples and negative samples in RF, they should be treated differently. Considering this, we propose an orthogonal complement components analysis (OCCA) combined with SVM in this paper. We then generalize the OCCA to Hilbert space and define the kernel empirical OCCA (KEOCCA). Through experiments on a Corel photo database with 17,800 images, we demonstrate that the proposed method can significantly improve the performance of conventional SVM-based RF.
Keywords :
Hilbert spaces; content-based retrieval; image retrieval; relevance feedback; visual databases; Corel photo database; Hilbert space; content-based image retrieval system; orthogonal complement component analysis; relevance feedback image retrieval; Content based retrieval; Hilbert space; Image analysis; Image databases; Image retrieval; Kernel; Negative feedback; Radio frequency; Support vector machine classification; Support vector machines;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on
ISSN :
1063-6919
Print_ISBN :
0-7695-2158-4
Type :
conf
DOI :
10.1109/CVPR.2004.1315217
Filename :
1315217
Link To Document :
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