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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. }
}