Movie story intensity representation through audiovisual tempo analysis

A comprehensive method for movie abstraction is developed in this research for applications in fast movie content exploring, indexing, browsing, and skimming, Most current approaches rely heavily on specific domain knowledge or models to identify and extract the determining scenes of a given movie; however, the segments extracted are often isolated, presenting a fragmented outline of the original. Our proposed method fuses simple audiovisual features, and measures the "tempos" of a movie directly, especially that of long-term ones. These tempos form a curve that catches the high-level semantics of a movie, indicating the events of interests named as "story intensity." Through tempo, the proposed algorithm provides a natural way that segments a movie into manageable parts. As our experimental results demonstrate, the condensed skimming clips efficiently extract semantic content that contains the most interesting and informative parts of the original movie.