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International Journal of Digital Multimedia Broadcasting
Volume 2012, Article ID 732514, 11 pages
http://dx.doi.org/10.1155/2012/732514
Research Article

Automatic Story Segmentation for TV News Video Using Multiple Modalities

UJF-Grenoble 1/UPMF-Grenoble 2/Grenoble INP, CNRS, LIG UMR 5217, 38041 Grenoble, France

Received 18 November 2011; Revised 13 March 2012; Accepted 12 April 2012

Academic Editor: Werner Bailer

Copyright © 2012 Émilie Dumont and Georges Quénot. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [15 citations]

The following is the list of published articles that have cited the current article.

  • Delphine Charlet, Geraldine Damnati, Abdessalam Bouchekif, and Ameur Douib, “Fusion of speaker and lexical information for topic segmentation: A co-segmentation approach,” 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5261–5265, . View at Publisher · View at Google Scholar
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  • Abdessalam Bouchekif, Géraldine Damnati, and Delphine Charlet, “Complementarity of lexical cohesion and speaker role information for story segmentation of French TV broadcast news,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7978, pp. 51–61, 2013. View at Publisher · View at Google Scholar
  • Vincent Claveau, and Sébastien Lefèvre, “Topic segmentation of TV-streams by watershed transform andvectorization,” Computer Speech & Language, 2014. View at Publisher · View at Google Scholar
  • Delphine Charlet, Geraldine Damnati, and Abdessalam Bouchekif, “Intra-content term weighting for topic segmentation,” ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, pp. 7113–7117, 2014. View at Publisher · View at Google Scholar
  • Minaz Parmar, and Marios C. Angelides, “MAC-REALM: A Video Content Feature Extraction and Modelling Framework,” Computer Journal, vol. 58, no. 9, pp. 2135–2170, 2015. View at Publisher · View at Google Scholar
  • Tarek Zlitni, Bassem Bouaziz, and Walid Mahdi, “Automatic topics segmentation for TV news video using prior knowledge,” Multimedia Tools and Applications, 2015. View at Publisher · View at Google Scholar
  • Bogdan Mocanu, Ruxandra Tapu, and Titus Zaharia, “Automatic Segmentation of TV News into Stories Using Visual and Temporal Information,” Advanced Concepts for Intelligent Vision Systems, vol. 10016, pp. 648–660, 2016. View at Publisher · View at Google Scholar
  • Chun-Yu Tsai, Ruilin Xu, John R. Kender, and Robert E. Colgan, “News event understanding by mining latent factors from multimodal tensors,” Iv and L-MM 2016 - Proceedings of the 2016 ACM Workshop on Vision and Language Integration Meets Multimedia Fusion, co-located with ACM Multimedia 2016, pp. 9–16, 2016. View at Publisher · View at Google Scholar
  • Adrian-Gabriel Chifu, and Sébastien Fournier, “SegChainW2V: Towards a Generic Automatic Video Segmentation Framework, Based on Lexical Chains of Audio Transcriptions and Word Embeddings,” Procedia Computer Science, vol. 96, pp. 1371–1380, 2016. View at Publisher · View at Google Scholar
  • Ridha Ejbali, Mounira Hmayda, and Mourad Zaied, “Automatic topics segmentation for TV news video,” Proceedings of SPIE - The International Society for Optical Engineering, vol. 10341, 2017. View at Publisher · View at Google Scholar
  • Pedro Almeida, Jorge Ferraz de Abreu, Rita Oliveira, and Diogo Gomes, “A video engine supported by social buzz to automatically create TV summaries,” Multimedia Tools and Applications, 2018. View at Publisher · View at Google Scholar
  • Tarek Zlitni, and Walid Mahdi, “Extraction and Annotation of News Topics From TV Streams for Web Video Sharing,” Knowledge-Intensive Economies and Opportunities for Social, Organizational, and Technological Growth, pp. 272–294, 2019. View at Publisher · View at Google Scholar