Scene detection using a large number of text features

Ichiro Yamada, Yohei Nakada, Atsushi Matsui, Takashi Matsumoto, Kikuka Miura, Hideki Sumiyoshi, Masahiro Shibata, Nobuyuki Yagi

Research output: Contribution to journalArticle


Broadcasting stations store a large volume of TV programs and manage them in their archives. To enable such programs to be used effectively, the technique for analyzing what is depicted in each scene plays a crucial role. TV programs often contain typical scenes which are used for specific purposes. This paper proposes a novel method of detecting such typical scenes by analyzing the context of closed captions. The proposed method handles a huge number of text features extracted from the closed captions through its use of a Monte Carlo based boosting algorithm. In experiments, we classified text segments extracted from the closed captions as to whether or not the corresponding scene is typical one. The results confirmed that our method classified with comparable accuracy to a conventional method using the AdaBoost algorithm and achieved a dramatic reduction in the learning time.

Original languageEnglish
Pages (from-to)157-166
Number of pages10
JournalITE Transactions on Media Technology and Applications
Issue number2
Publication statusPublished - 1 Jan 2013



  • Closed caption
  • GibbsBoost algorithm
  • Metadata
  • Scene detection
  • Tree structure analysis

Cite this

Yamada, I., Nakada, Y., Matsui, A., Matsumoto, T., Miura, K., Sumiyoshi, H., ... Yagi, N. (2013). Scene detection using a large number of text features. ITE Transactions on Media Technology and Applications, 1(2), 157-166.