NDLI: A framework for segmentation of talk and game shows

Content Provider	IEEE Xplore Digital Library
Author	Javed, O. Rasheed, Z. Shah, M.
Copyright Year	2001
Description	Author affiliation: Comput. Vision Lab., Central Florida Univ., Orlando, FL, USA (Javed, O.)
Abstract	In this paper, we present a method to remove commercials from talk and game show videos and to segment these videos into host and guest shots. In our approach, we mainly rely on information contained in shot transitions, rather than analyzing the scene content of individual frames. We utilize the inherent differences in scene structure of commercials and talk shows to differentiate between them. Similarly, we make use of the well-defined structure of talk shows, which can be exploited to classify shots as host or guest shots. The entire show is first segmented into camera shots based on color histogram. Then, we construct a data-structure (shot connectivity graph) which links similar shots over time. Analysis of the shot connectivity graph helps us to automatically separate commercials from program segments. This is done by first detecting stories, and then assigning a weight to each story based on its likelihood of being a commercial. Further analysis on stories is done to distinguish shots of the hosts from shots of the guests. We have tested our approach on several full-length shows (including commercials) and have achieved video segmentation with high accuracy. The whole scheme is fast and works even on low quality video (160/spl times/120 pixel images at 5 Hz).
Sponsorship	IEEE Comput. Soc. Tech. Committee on Pattern Anal. & Machine Intelligence
Starting Page	532
Ending Page	537
File Size	659155
Page Count	6
File Format	PDF
ISBN	0769511430
DOI	10.1109/ICCV.2001.937671
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2001-07-07
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Games Testing Image segmentation Software libraries Digital video broadcasting Computer vision Computer science Information analysis Layout Cameras
Content Type	Text
Resource Type	Article

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