Long-term learning in content-based image retrieval

Li, Jing and Allinson, Nigel (2008) Long-term learning in content-based image retrieval. International Journal of Imaging Systems and Technology, 18 (2-3). pp. 160-169. ISSN 08999457

Full content URL: http://onlinelibrary.wiley.com/doi/10.1002/ima.201...

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Item Type:Article
Item Status:Live Archive

Abstract

In content-based image retrieval, relevance feedback is an interactive process, which builds a bridge to connect users with the search engine. It leads to much improved retrieval performance by updating the query and the similarity measure according to a user's preference; and recently techniques have matured to some extent. However, most previous relevance feedback approaches exploit short-term learning (intraquery learning) that is dealing with the current feedback session but ignoring historical data from other users, which potentially results in a great loss of useful information. Fortunately, by recording and collecting feedback knowledge from different users over a variety of query sessions, long-term learning (interquery learning) can be implemented to further improve the performance of content-based image retrieval in terms of effectiveness and efficiency. For this reason, long-term learning has an increasingly important role in multimedia information searching. No comprehensive survey of long-term learning has been conducted to date. To this end, the article addresses this omission and offers suggestions for future work. © 2008 Wiley Periodicals, Inc.

Keywords:Computer software, Content based retrieval, Control theory, Feedback, Image recording, Image retrieval, Imaging techniques, Information retrieval, Information services, Search engines, Content-based image retrieval, Current feedback, Future work, Historical data, Long-term learning, Multimedia information, Relevance feedback, Retrieval performance, Similarity measuring, Education
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science > School of Computer Science
ID Code:8552
Deposited On:05 Apr 2013 10:15

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