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Video Copy Detection Based on Deep CNN Features and Graph-Based Sequence Matching

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Abstract

This paper introduces a novel content-based video copy detection method using the deep CNN features. An efficient deep CNN feature is employed to encode the image content while retaining the discrimination capability. Taking advantage of the extremely fast Euclidean distance similarity of deep CNN features, a keyframe-based copy retrieval method that exhaustively searches the copy candidates from the large keyframe database without indexing is proposed. Moreover, a graph-based sequence matching algorithm is employed to obtain the copy clips and accurately locate the video segments. The experimental evaluation has been performed to show the efficacy of the proposed deep CNN features. The promising results demonstrate the effectiveness of our proposed approach.

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Acknowledgements

I would like to thank Jun Lei for helpful discussions and encouragement. This work has been supported by the National Natural Science Foundation of China under Contract Nos. 61571453, 61202336 and by the Natural Science Foundation of Hunan province under Contract No. 14JJ3010.

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Authors and Affiliations

  1. College of Information System and Management, National University of Defense Technology, Changsha, Hunan, China

    Xin Zhang, Yuxiang Xie, Jingmeng He & Lili Zhang

  2. Department of Mathematics and Computer Science, Changsha University, Changsha, Hunan, China

    Xidao Luan

  3. The Key Lab, The Academy of Equipment Command and Technology, Beijing, China

    Lingda Wu

Authors
  1. Xin Zhang
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  2. Yuxiang Xie
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  3. Xidao Luan
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  4. Jingmeng He
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  5. Lili Zhang
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  6. Lingda Wu
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Correspondence to Yuxiang Xie.

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Zhang, X., Xie, Y., Luan, X. et al. Video Copy Detection Based on Deep CNN Features and Graph-Based Sequence Matching. Wireless Pers Commun 103, 401–416 (2018). https://doi.org/10.1007/s11277-018-5450-x

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