A Statistical Framework for Video Skimming Based on Logical Story Units and Motion Activity

In this work we present a method for video skimming based on hidden Markov Models (HMMs) and motion activity. Specifically, a set of HMMs is used to model subsequent logical story units, where the HMM states represent different visual-concepts, the transitions model the temporal dependencies in each story unit, and stochastic observations are given by single shots. The video skim is generated as an observation sequence, where, in order to privilege more informative segments for entering the skim, dynamic shots are assigned higher probability of observation. The effectiveness of the method is demonstrated on a video set from different kinds of programmes, and results are evaluated in terms of metrics that measure the content representational value of the obtained video skims.