Digital video segmentation

Authors:
A. Hampapur;T. Weymouth;R. Jain
Affiliations:
Artificial Intelligence Laboratory, Electrical Engineering and Computer Science, University of Michigan, 1101 Beal Ave, Ann Arbor, MI;Artificial Intelligence Laboratory, Electrical Engineering and Computer Science, University of Michigan, 1101 Beal Ave, Ann Arbor, MI;University of California at San Diego, La Jolla, CA and Artificial Intelligence Laboratory, Electrical Engineering and Computer Science, University of Michigan, 1101 Beal Ave, Ann Arbor, MI
Venue:
MULTIMEDIA '94 Proceedings of the second ACM international conference on Multimedia
Year:
1994

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Abstract

The data driven, bottom up approach to video segmentation has ignored the inherent structure that exists in video. This work uses the model driven approach to digital video segmentation. Mathematical models of video based on video production techniques are formulated. These models are used to classify the edit effects used in video and film production. The classes and models are used to systematically design the feature detectors for detecting edit effects in digital video. Digital video segmentation is formulated as a feature based classification problem. Experimental results from segmenting cable television programming with cuts, fades, dissolves and page translate edits are presented.