Hidden Markov Models for Video Skim Generation

In this paper we present a statistical framework based on hidden Markov models (HMMs) for video skimming. A chain of HMMs is used to model subsequent story units: HMM states represent different visual-concepts, transitions model the temporal dependencies in each story unit, and stochastic observations are given by single shots. The 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 assess the content representational value of the obtained video skims.