Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
5-2004
Abstract
Without textual descriptions or label information of images, searching semantic concepts in image databases is still a very challenging task. While automatic annotation techniques are yet a long way off, we can seek other alternative techniques to solve this difficult issue. In this paper, we propose to learn Web images for searching the semantic concepts in large image databases. To formulate effective algorithms, we suggest to engage the support vector machines for attacking the problem. We evaluate our algorithm in a large image database and demonstrate the preliminary yet promising results.
Keywords
Web Image Learning, Semantic Searching, Image Retrieval, RelevanceFeedback, Support Vector Machine
Discipline
Computer Sciences | Databases and Information Systems
Research Areas
Data Science and Engineering
Publication
WWW Alt 2004: Proceedings of the 13th International World Wide Web Conference on Alternate Track Papers & Posters: New York, May 19-21
First Page
406
Last Page
407
ISBN
9781581139129
Identifier
10.1145/1013367.1013498
Publisher
ACM
City or Country
New York
Citation
HOI, Steven and LYU, Michael R..
Web Image Learning for Searching Semantic Concepts in Image Databases. (2004). WWW Alt 2004: Proceedings of the 13th International World Wide Web Conference on Alternate Track Papers & Posters: New York, May 19-21. 406-407.
Available at: https://ink.library.smu.edu.sg/sis_research/2396
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/1013367.1013498