Sunset scene classification using simulated image recomposition

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.