Using image-transform-based bootstrapping to improve scene classification

Jiebo Luo; Matthew Boutell; Robert T. Gray; Christopher Brown

doi:10.1117/12.527022

18 December 2003 Using image-transform-based bootstrapping to improve scene classification

Jiebo Luo, Matthew Boutell, Robert T. Gray, Christopher Brown

Proceedings Volume 5307, Storage and Retrieval Methods and Applications for Multimedia 2004; (2003) https://doi.org/10.1117/12.527022
Event: Electronic Imaging 2004, 2004, San Jose, California, United States

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.

Citation Download Citation

Jiebo Luo, Matthew Boutell, Robert T. Gray, and Christopher Brown "Using image-transform-based bootstrapping to improve scene classification", Proc. SPIE 5307, Storage and Retrieval Methods and Applications for Multimedia 2004, (18 December 2003); https://doi.org/10.1117/12.527022

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