Automated classification of digital video is emerging as an important piece of the puzzle in the design of content management systems for digital libraries. The ability to classify videos into various genres such as sports, news, movies, or documentaries increases the efficiency of indexing, browsing, and retrieval of video in large databases. In this paper, we present an automated technique for identifying slow-motion replays directly from the compressed domain of MPEG video. It uses the macroblock, motion, and bit-rate information that is readily accessible from MPEG video with very minimal decoding, leading to enormous gains in processing speeds.
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