Publication Type

Conference Proceeding Article

Publication Date

6-2005

Abstract

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.

Keywords

image colour analysis, image retrieval, learning (artificial intelligence), support vector machines, visual databases

Discipline

Computer Sciences | Databases and Information Systems

Research Areas

Data Management and Analytics

Publication

CVPR 2005: IEEE Computer Society Conference on Computer Vision and Pattern Recognition: 20-25 June 2005, San Diego, CA

First Page

302

Last Page

309

ISBN

9780769523729

Identifier

10.1109/CVPR.2005.44

Publisher

IEEE Computer Society

City or Country

Los Alamitos, CA

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://dx.doi.org/10.1109/CVPR.2005.44

Share

COinS