Making large-scale support vector machine learning practical
Advances in kernel methods
My digital photos: where and when?
Proceedings of the 13th annual ACM international conference on Multimedia
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
Incorporating concept ontology into multi-level image indexing
Proceedings of the First International Conference on Internet Multimedia Computing and Service
Classifying high quality photographs by creative exposure themes
FDIA'09 Proceedings of the Third BCS-IRSG conference on Future Directions in Information Access
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The automatic point-and-click mode of Digital Still Cameras (DSCs) may be a boon to most users whom are simply trigger-happy. However, this automatic mode may not generate the best photos possible or be even applicable for certain types of shots, especially those that require technical expertise. To bridge this gap, many DSCs now offer "Scene Modes" that would easily allow the user to effortlessly configure his camera to specifically take certain types of photos, usually resulting in better quality pictures. These "Scene Modes" provide valuable contextual information about these types of photos and in this paper, we examine how we could make use of "Scene Modes" to assist in generic Image Scene Classification for photos taken on expert/manual settings. Our algorithm could be applied to any image classes associated with the "Scene Modes" and we demonstrated this with the classification of fireworks photos in our case study.