ACM Multimedia 97 - Electronic Proceedings
November 8-14, 1997Crowne Plaza Hotel, Seattle, USA
Combining Supervised Learning with Color Correlograms for Content-Based Image Retrieval
- Jing Huang
- Department of Computer Science
- Cornell University
- Ithaca, NY 14853.
- (607) 255 1158
- huang@cs.cornell.edu
- http://www.cs.cornell.edu/home/huang/huang.html
- S Ravi Kumar
- Department of Computer Science
- Cornell University
- Ithaca, NY 14853.
- (607) 255 1158
- ravi@cs.cornell.edu
- http://www.cs.cornell.edu/home/ravi/ravi.html
- Mandar Mitra
- Department of Computer Science
- Cornell University
- Ithaca, NY 14853.
- (607) 255 1158
- mitra@cs.cornell.edu
Abstract
The paper addresses how relevance feedback can be used to
improve the performance of content-based image retrieval. We present two
supervised learning methods: learning the query and
learning the metric . We combine the learning methods with the
recently proposed color correlograms for image
indexing/retrieval. Our results on a large image database of over
20,000 images suggest that these learning methods are quite
effective for content-based image retrieval.
Keywords
Content-based Image Retrieval, Image Indexing.