Clustering of scene repeats for essential rushes preview

This paper focuses on a specific type of unedited video content, called rushes, which are used for movie editing and usually present a high-level of redundancy. Our goal is to automatically extract a summarized preview, where redundant material is diminished without discarding any important event. To achieve this, rushes content has been first analysed and modeled. Then different clustering techniques on shot key-frames are presented and compared in order to choose the best representative segments to enter the preview. Experiments performed on TRECVID data are evaluated by computing the mutual information between the obtained results and a manually annotated ground-truth.