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Proceedings Paper

Parsing TV programs for identification and removal of nonstory segments
Author(s): Thomas McGee; Nevenka Dimitrova
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Paper Abstract

Abstracting video information automatically from TV broadcast, requires reliable methods for isolating program and commercial segments out of the full broadcast material. In this paper, we present the results from cut, static sequence, black frame, and text detection, for the purpose of isolating non-program segments. These results are evaluated, by comparison, to human visual inspection using more than 13 hours of varied program content. Using cut rate detection alone, produced a high recall with medium precision. Text detection was performed on the commercials, and the false positive segments. Adding text detection slightly lowers the recall. However, much higher precision is achieved. A new fast black frame detector algorithm is presented. Black frame detection is important for identifying commercial boundaries. Results indicate that adding detection of text, in addition to cut rate, to reduce the number of false positives, appears to be a promising method. Furthermore, by adding the information about position and size of text, and tracking it through an area, should further increase reliability.

Paper Details

Date Published: 17 December 1998
PDF: 9 pages
Proc. SPIE 3656, Storage and Retrieval for Image and Video Databases VII, (17 December 1998); doi: 10.1117/12.333844
Show Author Affiliations
Thomas McGee, Philips Research (United States)
Nevenka Dimitrova, Philips Research (United States)


Published in SPIE Proceedings Vol. 3656:
Storage and Retrieval for Image and Video Databases VII
Minerva M. Yeung; Boon-Lock Yeo; Charles A. Bouman, Editor(s)

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