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The International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery

ICNC-FSKD 2019: Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery pp 361–368Cite as

Towards Data-Driven Automatic Video Editing

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 1074)

Abstract

Automatic video editing involving at least the steps of selecting the most valuable footage from points of view of visual quality and the importance of action filmed; and cutting the footage into a brief and coherent visual story that would be interesting to watch is implemented in a purely data-driven manner. Visual semantic and aesthetic features are extracted by the ImageNet-trained convolutional neural network, and the editing controller is trained by an imitation learning algorithm. As a result, at test time the controller shows the signs of observing basic cinematography editing rules learned from the corpus of motion pictures masterpieces.

Keywords

  • Automatic video editing
  • Convolutional neural networks
  • Reinforcement learning

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Author information

Authors and Affiliations

  1. Cinema and Photo Research Institute, 47 Leningradsky Ave., Moscow, 126167, Russia

    Sergey Podlesnyy

Authors
  1. Sergey Podlesnyy
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Corresponding author

Correspondence to Sergey Podlesnyy .

Editor information

Editors and Affiliations

  1. School of Computer Science and Engineering, The University of Aizu, Aizu-Wakamatsu, Fukushima, Japan

    Dr. Yong Liu

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Dr. Lipo Wang

  3. Computer Science and Mathematics, University of Sao Paulo, Ribeirao Preto, Brazil

    Dr. Liang Zhao

  4. School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China

    Prof. Zhengtao Yu

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Podlesnyy, S. (2020). Towards Data-Driven Automatic Video Editing. In: Liu, Y., Wang, L., Zhao, L., Yu, Z. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2019. Advances in Intelligent Systems and Computing, vol 1074. Springer, Cham. https://doi.org/10.1007/978-3-030-32456-8_39

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