Event-driven video abstraction and visualization

In this paper, we propose a new video summarization procedure that produces a dynamic (video) abstract of the original video sequence. Our technique compactly summarizes a video data by preserving its original temporal characteristics (visual activity) and semantically essential information. It relies on an adaptive nonlinear sampling. The local sampling rate is directly proportional to the amount of visual activity in localized sub-shot units of the video. To get very short, yet semantically meaningful summaries, we also present an event-oriented abstraction scheme, in which two semantic events; emotional dialogue and violent action, are characterized and abstracted into the video summary before all other events. If the length of the summary permits, other non key events are then added. The resulting video abstract is highly compact.