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Advanced news video parsing via visual characteristics of anchorperson scenes

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Abstract

In this paper, we present an advanced news video parsing system via exploring the visual characteristics of anchorperson scenes, which aims to provide personalized news services over Internet or mobile platforms. As the anchorperson shots serve as the root shots for constructing news video, the addressed system firstly performs anchorperson detection which divides the news into several segments. Due to the manipulation of multi-features and post-processing, our method of anchorperson detection can even be efficiently applied to news video whose anchorperson scenes are most challenging and complicated. Usually, the segments produced from anchorperson detection are regarded as news stories. However, an observation in our database proves this is not true because of the existing of interview scenes. These interview scenes are showed in the form that interviewer (anchorperson) and interviewee recursively appear. Thus, a technique called interview clustering based on face similarity is carried out to merge these interview segments. Another novel aspect of our system is entity summarization of interview scenes. We adopt it in the system at final. The effectiveness and robustness of the proposed system are demonstrated by the evaluation on 19 hours of news programs from 6 different TV Channels.

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Acknowledgements

This work was supported by both Invenio project launched by France Telecom R&D (Orange Labs), and The Key Project of The National Natural Science Foundation of China (90920001).

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Authors and Affiliations

  1. Beijing University of Posts and Telecommunications, Beijing, China

    Yuan Dong, Gang Qin & Guorui Xiao

  2. Corporate Research, Huawei Tech Co., Shenzhen, China

    Shiguo Lian

  3. France Telecom R&D, Beijing, China

    Xiaofu Chang

Authors
  1. Yuan Dong
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  2. Gang Qin
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  3. Guorui Xiao
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  4. Shiguo Lian
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  5. Xiaofu Chang
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Correspondence to Yuan Dong.

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Dong, Y., Qin, G., Xiao, G. et al. Advanced news video parsing via visual characteristics of anchorperson scenes. Telecommun Syst 54, 247–263 (2013). https://doi.org/10.1007/s11235-013-9731-0

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