Abstract
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Film genres in digital video can be detected
automatically. In a three-step approach we analyze first the syntactic
properties of digital films: color statistics, cut detection, camera
motion, object motion and audio. In a second step we use these
statistics to derive at a more abstract level film style attributes
such as camera panning and zooming, speech and music. These are
distinguishing properties for film genres, e.g. newscasts vs. sports
vs. commercials. In the third and final step we map the detected style
attributes to film genres. Algorithms for the three steps are
presented in detail, and we report on initial experience with real
videos. It is our goal to automatically classify the large body of
existing video for easier access in digital video-on-demand databases.
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