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Proceedings Paper

Content-based image retrieval via adaptive multifeature templates
Author(s): Zijun Yang; C.-C. Jay Kuo
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Paper Abstract

The use of image content analysis and image clustering techniques to organize an image database with a great variety of collections is investigated in this work. The objective is to bridge the gap between low-level features and their high level semantic meanings. We attempt this goal by using both coarse and fine classifications in image database organization. Image content analysis serves as the major tool in coarse classification. A set of typical image collections are studied by training their low-level feature vectors. Clusters of representative low-level features are further provided in form of semantic templates to provide fine-level classification clues for achieving a good query performance and serving as a supporting tool for browsing. With these multiple feature semantic templates, an interactive retrieval process can be conveniently implemented to incorporate user's feedback to achieve the desired query.

Paper Details

Date Published: 24 August 1999
PDF: 12 pages
Proc. SPIE 3846, Multimedia Storage and Archiving Systems IV, (24 August 1999); doi: 10.1117/12.360428
Show Author Affiliations
Zijun Yang, Univ. of Southern California (United States)
C.-C. Jay Kuo, Univ. of Southern California (United States)


Published in SPIE Proceedings Vol. 3846:
Multimedia Storage and Archiving Systems IV
Sethuraman Panchanathan; Shih-Fu Chang; C.-C. Jay Kuo, Editor(s)

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