Toward High-Level Visual Information Retrieval

Content-based visual information retrieval (CBVIR) as a new generation (with new concepts, techniques, mechanisms, etc.) of visual information retrieval has attracted many interests from the database community. The research starts by using a low-level feature from more than a dozen years ago. The current focus has shifted to capture high-level semantics of visual information. This chapter will convey the research from the feature level to the semantic level by treating the problem of semantic gap under the general framework of CBVIR. This high-level research is the so-called semantic-based visual information retrieval (SBVIR). This chapter first shows some statistics about the research publications on semantic-based retrieval in recent years; it then presents some existing approaches based on multi-level image retrieval and multi-level video retrieval. It also gives an overview of several current centers of attention by summarizing certain results on subjects such as image and video 701 E. Chocolate Avenue, Suite 200, Hershey PA 17033-1240, USA Tel: 717/533-8845; Fax 717/533-8661; URL-http://www.irm-press.com ITB13850 IRM PRESS This chapter appears in the book, Semantic-Based Visual Information Retrieval by Y.J. Zhang © 2007, Idea Group Inc.