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Proceedings Paper

TV news story segmentation based on a simple statistical model
Author(s): Xiaoye Lu; Zhe Feng; Xingquan Zhu; Lide Wu
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Paper Abstract

TV News is a well-structured media, since it has distinct boundaries of semantic units (news stories) and relatively constant content structure. Hence, an efficient algorithm to segment and analyze the structure information among news videos would be necessary for indexing or retrieving a large video database. Lots of researches in this area have been done by using close-caption, speech recognition or Video-OCR to obtain the semantic content, however, these methods put much emphasis on obtaining the text and NLP for semantic understanding. Here, in this paper, we try to solve the problem by integrating statistic model and visual features. First, a video caption and anchorperson shot detection method is presented, after that, a statistic model is used to describe the relationship between the captions and the news story boundaries, then, a news story segmentation method is introduced by integrating all these aforementioned results. The experiment results have proved that the method can be used in acquiring most of the structure information in News programs.

Paper Details

Date Published: 20 December 2001
PDF: 8 pages
Proc. SPIE 4672, Internet Imaging III, (20 December 2001); doi: 10.1117/12.452681
Show Author Affiliations
Xiaoye Lu, Fudan Univ. (China)
Zhe Feng, Fudan Univ. (China)
Xingquan Zhu, Purdue Univ. (United States)
Lide Wu, Fudan Univ. (China)


Published in SPIE Proceedings Vol. 4672:
Internet Imaging III
Giordano B. Beretta; Raimondo Schettini, Editor(s)

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