Video skimming based on story units via general tempo analysis


A skimming system for movie content exploration is proposed using story units extracted via general tempo analysis of audio and visual data. Quite a few schemes have been proposed to segment video data into shots with low-level features, yet the grouping of shots into meaningful units, called story units here, is important and challenging. In this work, we detect similar shots using keyframes and include these similar shots as a node. Then, an importance measure is calculated based on the total length of each node. Finally, we select sinks and shots according to this measure. Based on these semantic shots, a meaningful skim can be successfully generated. Simulation results are presented to show that the proposed video skimming scheme can preserve the essential and significant content of the original video data


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