Web image learning for searching semantic concepts in image databases

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. Categories and Subject Descriptors H.3.3 [Information Systems]: Information Search and Retrieval-Clustering, Query Reformation, Relevance Feedback General Terms Design, Algorithms, Experimentation Keywords Web Image Learning, Semantic Searching, Image Retrieval, Relevance Feedback, Support Vector Machine

• Type: Conference
• Year: 2004
• Where: WWW
• Authors: Chu-Hong Hoi, Michael R. Lyu
• Keywords: Internet Technology, Keywords Web Image, Large Image Databases, Semantic Concepts, WWW 2004