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Sunset Scene Classification Using Simulated Image Recomposition |
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@InProceedings{boutell:03icme,
author = {Matthew Boutell and Jiebo Luo and Robert T. Gray},
title = {Sunset Scene Classification Using Simulated Image Recomposition},
booktitle = {International Conference on Multimedia Expo},
year = 2003,
address = {Baltimore, MD},
month = {July},
abstract = { Knowledge of the semantic classification of an image can be used to
improve the accuracy of queries in content-based image organization and
retrieval and to provide customized image enhancement. We developed an
exemplar-based system for classifying sunset scenes. However, the
performance of such a system depends largely on the size and quality of
the set of training exemplars, which can be limited in practice. In
addition, variations in scene content, as well as distracting regions, may
exist in many testing images to prohibit good matches with the exemplars.
We propose using simulated spatial and temporal image recomposition to
address such issues. The recomposition schemes boost the recall of sunset
images from a reasonably large data set by 10%, while holding the false
positive rate constant. }
}