Conference Proceeding Article
Although recent studies have shown that unlabeled data are beneficial to boosting the image retrieval performance, very few approaches for image retrieval can learn with labeled and unlabeled data effectively. This paper proposes a novel semi-supervised active learning framework comprising a fusion of semi-supervised learning and support vector machines. We provide theoretical analysis of the active learning framework and present a simple yet effective active learning algorithm for image retrieval. Experiments are conducted on real-world color images to compare with traditional methods. The promising experimental results show that our proposed scheme significantly outperforms the previous approaches.
image colour analysis, image retrieval, learning (artificial intelligence), support vector machines, visual databases
Computer Sciences | Databases and Information Systems
Data Management and Analytics
CVPR 2005: IEEE Computer Society Conference on Computer Vision and Pattern Recognition: 20-25 June 2005, San Diego, CA
IEEE Computer Society
City or Country
Los Alamitos, CA
HOI, Steven and LYU, Michael R..
A Semi-Supervised Active Learning Framework for Image Retrieval. (2005). CVPR 2005: IEEE Computer Society Conference on Computer Vision and Pattern Recognition: 20-25 June 2005, San Diego, CA. 302-309. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2394
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