Long-term learning in content-based image retrieval

✍ Scribed by Jing Li; Nigel M. Allinson

• Publisher: John Wiley and Sons
• Year: 2008
• Tongue: English
• Weight: 476 KB
• Volume: 18
• Category: Article
• ISSN: 0899-9457
• DOI: 10.1002/ima.20148

✦ Synopsis

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. Int J Imaging Syst Technol, 18, 160–169, 2008