Unsupervised and model-free news video segmentation
Refereed conference paper presented and published in conference proceedings


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AbstractBased on a simple temporal structural model of news program, this paper presents a practical solution to automatic news story segmentation by integrating syntactic and semantic methods. First, a syntactic segmentation method is used to detect the shot boundaries in order to partition video frames into video shots. Then a semantic segmentation method based on the graph-theoretical cluster analysis is developed to classify the video shots into anchorperson shots and news footage shots. Finally, a structural model of news video is used to complete the news-story segmentation. The proposed method obtains a precision of 90.45% and a recall of 95.83% in the segmentation experiment of 168 news stories from two Hong Kong news stations.
All Author(s) ListGao XB, Tang X
Name of ConferenceIEEE Workshop on Content-Based Access of Image and Video Libraries (CBAIVL 2001)
Start Date of Conference14/12/2001
Place of ConferenceKAUAI
Country/Region of ConferenceUnited States of America
Detailed descriptionIEEE Workshop on Content-Based Access of Image and Video Libraries 2001
Year2001
Month1
Day1
PublisherIEEE COMPUTER SOC
Pages58 - 64
ISBN0-7695-1354-9
LanguagesEnglish-United Kingdom
Web of Science Subject CategoriesComputer Science; Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering; Engineering, Electrical & Electronic; Imaging Science & Photographic Technology

Last updated on 2020-20-10 at 02:35