This paper proposes shot classification and scene boundary/genre identification for MPEG compressed movies. Through statistical analysis of audio-visual features on compressed domain, the proposed method achieves subjectively accurate shot classification within the movies into a predefined genre set, as well as scene segmentation based on the shot classification results. By feeding subjectively evaluated feature vectors for each genre into the decision tree classifier, each shot is classified at very low computational cost. Then a sequence of shots belonging to the same genre is determined as a scene. The experimental results show that most of the shots in the movies are classified into subjectively accurate genres, and also that the scene segmentation results are more accurate and robust than the conventional approach.
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