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Proceedings Paper

Using image-transform-based bootstrapping to improve scene classification
Author(s): Jiebo Luo; Matthew Boutell; Robert T. Gray; Christopher Brown
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Paper Abstract

The performance of an exemplar-based scene classification system depends largely on the size and quality of its set of training exemplars, which can be limited in practice. In addition, in non-trivial data sets, variations in scene content as well as distracting regions may exist in many testing images to prohibit good matches with the exemplars. We introduce the concept of image-transform bootstrapping using image transforms to address such issues. In particular, three major schemes are described for exploiting this concept to augment training, testing, and both. We have successfully applied it to three applications of increasing difficulty: sunset detection, outdoor scene classification, and automatic image orientation detection. It is shown that appropriate transforms and meta-classification methods can be selected to boost performance according to the domain of the problem and the features/classifier used.

Paper Details

Date Published: 18 December 2003
PDF: 12 pages
Proc. SPIE 5307, Storage and Retrieval Methods and Applications for Multimedia 2004, (18 December 2003); doi: 10.1117/12.527022
Show Author Affiliations
Jiebo Luo, Eastman Kodak Co. (United States)
Matthew Boutell, Univ. of Rochester (United States)
Robert T. Gray, Eastman Kodak Co. (United States)
Christopher Brown, Univ. of Rochester (United States)


Published in SPIE Proceedings Vol. 5307:
Storage and Retrieval Methods and Applications for Multimedia 2004
Minerva M. Yeung; Rainer W. Lienhart; Chung-Sheng Li, Editor(s)

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