Publication Type
Conference Proceeding Article
Version
acceptedVersion
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
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
Citation
HOI, Steven; CHAN, Chi-Hang; HUANG, Kaizhu; LYU, Michael R.; and KING, Irwin.
Biased Support Vector Machine for Relevance Feedback in Image Retrieval. (2004). 2004 IEEE International Joint Conference on Neural Networks: Proceedings: Budapest, Hungary, 25-29 July, 2004. 3189-3194.
Available at: https://ink.library.smu.edu.sg/sis_research/2399
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://dx.doi.org/10.1109/IJCNN.2004.1381186