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Automatic topics segmentation for TV news video using prior knowledge

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Abstract

TV streams represent a principal source of multimedia information. The goal of the proposed approach is to enable a better exploitation of this source of video by multimedia services (i.e., TV-On-Demand, catch-up TV), social community, and video-sharing platforms (Vimeo, Youtube, Facebook …). In this work, we present an automatic structuring approach of TV news. The originality of the approach is the use of the contextual and operational characteristics as prior knowledge. This knowledge is modeled as video grammar which governs the structuring of TV stream content. This structuring is carried out on two levels. The first level identifies news programs in TV stream. The second level aims to identify the internal structure of the identified news programs. At this level, we opt to treat the case of TV news programs due to the large audience because of pertinent information within. Comparison experiments to similar works have been carried out on the TRECVID 2003 database. We show significant improvements to TV news structuring exceed 90 %.

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Authors and Affiliations

  1. Higher Institute of Information Technology and Multimedia, University of Sfax, Sfax, Tunisia

    Tarek Zlitni & Bassem Bouaziz

  2. College of Computers and Information Technology, Taif University, KSA, Taif, Saudi Arabia

    Walid Mahdi

  3. MIRACL Laboratory, Sfax, Tunisia

    Tarek Zlitni, Bassem Bouaziz & Walid Mahdi

Authors
  1. Tarek Zlitni
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  2. Bassem Bouaziz
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  3. Walid Mahdi
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Correspondence to Tarek Zlitni.

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Zlitni, T., Bouaziz, B. & Mahdi, W. Automatic topics segmentation for TV news video using prior knowledge. Multimed Tools Appl 75, 5645–5672 (2016). https://doi.org/10.1007/s11042-015-2531-7

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  • DOI: https://doi.org/10.1007/s11042-015-2531-7

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