Publication Type

Conference Proceeding Article

Publication Date

7-2004

Abstract

Recently, support vector machines (SVMs) have been engaged on relevance feedback tasks in content-based image retrieval. Typical approaches by SVMs treat the relevance feedback as a strict binary classification problem. However, these approaches do not consider an important issue of relevance feedback, i.e. the unbalanced dataset problem, in which the negative instances largely outnumber the positive instances. For solving this problem, we propose a novel technique to formulate the relevance feedback based on a modified SVM called biased support vector machine (Biased SVM or BSVM). Mathematical formulation and explanations are provided for showing the advantages. Experiments are conducted to evaluate the performance of our algorithms, in which promising results demonstrate the effectiveness of our techniques.

Keywords

content-based retrieval, image classification, image retrieval, relevance feedback, support vector machines

Discipline

Computer Sciences | Databases and Information Systems

Research Areas

Data Management and Analytics

Publication

2004 IEEE International Joint Conference on Neural Networks: Proceedings: Budapest, Hungary, 25-29 July, 2004

First Page

3189

Last Page

3194

ISBN

9780780383593

Identifier

10.1109/IJCNN.2004.1381186

Publisher

IEEE

City or Country

Piscataway, NJ

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/IJCNN.2004.1381186

Share

COinS