Video genre classification using dynamics

The problem addressed here is the classification of videos at the highest level into pre-defined genre. The approach adopted is based on the dynamic content of short sequences (/spl sim/30 secs). This paper presents two methods of extracting motion from a video sequence: foreground object motion and background camera motion. These dynamics are extracted, processed and applied to classify 3 broad classes: sports, cartoons and news. Experimental results for this 3 class problem give error rates of 17%, 8% and 6% for camera motion, object motion and both combined respectively, on /spl sim/30 second sequences.

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