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.
Web Image Learning, Semantic Searching, Image Retrieval, RelevanceFeedback, Support Vector Machine
In Proceedings of 13th International World Wide Web Conference (WWW2004)
City or Country
New York, USA
HOI, Steven and Lyu, Michael R..
Web Image Learning for Searching Semantic Concepts in Image Databases. (2004). In Proceedings of 13th International World Wide Web Conference (WWW2004). Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2396