Orthogonal complement component analysis for positive samples in SVM based relevance feedback image retrieval

Publication Type:
Conference Proceeding
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004, 2
Issue Date:
Full metadata record
Files in This Item:
Filename Description Size
Thumbnail2011001294OK.pdf379.31 kB
Adobe PDF
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.
Please use this identifier to cite or link to this item: