Orthogonal Complement Component Analysis for Positive Samples in SVM Based Relevance Feedback Image Retrieval
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.
Cite
Text
Tao and Tang. "Orthogonal Complement Component Analysis for Positive Samples in SVM Based Relevance Feedback Image Retrieval." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2004. doi:10.1109/CVPR.2004.181Markdown
[Tao and Tang. "Orthogonal Complement Component Analysis for Positive Samples in SVM Based Relevance Feedback Image Retrieval." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2004.](https://mlanthology.org/cvpr/2004/tao2004cvpr-orthogonal/) doi:10.1109/CVPR.2004.181BibTeX
@inproceedings{tao2004cvpr-orthogonal,
title = {{Orthogonal Complement Component Analysis for Positive Samples in SVM Based Relevance Feedback Image Retrieval}},
author = {Tao, Dacheng and Tang, Xiaoou},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2004},
pages = {586-591},
doi = {10.1109/CVPR.2004.181},
url = {https://mlanthology.org/cvpr/2004/tao2004cvpr-orthogonal/}
}