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Automatic Interactive Video Authoring Method via Object Recognition

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Intelligent Information and Database Systems (ACIIDS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10191))

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Abstract

Interactive video is a type of video which provides interactions for obtaining video related information or participating in video content. However, authors of interactive video need to spend much time to create the interactive video content. Many researchers have presented methods and features to solve the time-consuming problem. However, the methods are still too complicated to use and need to be automated. In this paper, we suggest an automatic interactive video authoring method via object recognition. Our proposed method uses deep learning based object recognition and an NLP-based keyword extraction method to annotate objects. To evaluate the method, we manually annotated the objects in the selected video clips, and we compared proposed method and manual method. The method achieved an accuracy rate of 43.16% for the whole process. This method allows authors to create interactive videos easily.

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Author information

Authors and Affiliations

  1. Department of Information Engineering, Inha University, Incheon, South Korea

    Ui-Nyoung Yoon, Myung-Duk Hong & Geun-Sik Jo

Authors
  1. Ui-Nyoung Yoon
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  2. Myung-Duk Hong
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  3. Geun-Sik Jo
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Corresponding author

Correspondence to Geun-Sik Jo .

Editor information

Editors and Affiliations

  1. Wrocław University of Science and Technology , Wroclaw, Poland

    Ngoc Thanh Nguyen

  2. Japan Advanced Institute of Science and Technology , Nomi, Japan

    Satoshi Tojo

  3. Japan Advanced Institute of Science and Technology , Nomi, Japan

    Le Minh Nguyen

  4. Wrocław University of Science and Technology , Wrocław, Poland

    Bogdan Trawiński

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Yoon, UN., Hong, MD., Jo, GS. (2017). Automatic Interactive Video Authoring Method via Object Recognition. In: Nguyen, N., Tojo, S., Nguyen, L., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2017. Lecture Notes in Computer Science(), vol 10191. Springer, Cham. https://doi.org/10.1007/978-3-319-54472-4_55

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  • DOI: https://doi.org/10.1007/978-3-319-54472-4_55

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54471-7

  • Online ISBN: 978-3-319-54472-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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