Image classification and querying using composite region templates
Computer Vision and Image Understanding - Special issue on content-based access for image and video libraries
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Content-Based Ima e Orientation Detection with Support Vector Machines
CBAIVL '01 Proceedings of the IEEE Workshop on Content-based Access of Image and Video Libraries (CBAIVL'01)
Recognition of Images in Large Databases Using a Learning Framework
Recognition of Images in Large Databases Using a Learning Framework
Content-Based Hierarchical Classification of Vacation Images
ICMCS '99 Proceedings of the IEEE International Conference on Multimedia Computing and Systems - Volume 2
Beyond pixels: Exploiting camera metadata for photo classification
Pattern Recognition
Bayesian fusion of camera metadata cues in semantic scene classification
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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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.