Automatic video knowledge mining for summary generation based on un-supervised statistical learning

The summary of video content provides an effective way to speed up video browsing and comprehension. In this paper, we propose a novel automatic video summarization approach. Video structure is first analyzed by combining spatial-temporal analysis and statistical learning. Video scenes are then detected based on unsupervised statistical learning. The video summary is created by selecting the most informative shots from the video scenes that are modeled as a directed graph. Experiments show that the proposed approach can generate the most concise and informative video summary.