Web Image Learning for Searching Semantic Concepts in Image Databases

• Main Authors: HOI, Steven, LYU, Michael R.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2004
• Subjects: Web Image Learning; Semantic Searching; Image Retrieval; RelevanceFeedback; Support Vector Machine; Computer Sciences; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/2396; https://ink.library.smu.edu.sg/context/sis_research/article/3396/viewcontent/www2004_steven.pdf
• Institution: Singapore Management University

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.