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.
A digital photo can "tell a thousand words" through the use of its metadata and as it is usually part of a collection, metadata management, reuse, propagation & inference could be achieved via its association with a collection. However, there is not much work on metadata management, reuse, propagation & inference, particularly on a group basis. In this paper, we proposed a collection-oriented metadata framework which provides a basis for metadata management, reuse, propagation & inference and demonstrated the utility of such a framework.
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