Main > Laser & Optoelectronics Progress >  Volume 55 >  Issue 1 >  Page 011006 > Article
  • Abstract
  • Abstract
  • View Summary
  • Figures (8)
  • Tables (2)
  • Equations (0)
  • References (18)
  • Get PDF
  • Paper Information
  • Received: Jul. 18, 2017

    Accepted: --

    Posted: Jan. 1, 2018

    Published Online: Sep. 10, 2018

    The Author Email: Wang Dongdong (, Li Yuenan (

    DOI: 10.3788/LOP55.011006

  • Get Citation
  • Copy Citation Text

    Dongdong Wang, Yuenan Li. Video Fingerprint Algorithm Based on Spatio-Temporal Deep Neural Network[J]. Laser & Optoelectronics Progress, 2018, 55(1): 011006

    Download Citation

  • Category
  • Image Processing
  • Share
Laser & Optoelectronics Progress, Vol. 55, Issue 1, 011006 (2018)

Video Fingerprint Algorithm Based on Spatio-Temporal Deep Neural Network

Wang Dongdong*and Li Yuenan

Author Affiliations

  • School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China


With the development of content-sharing networks, the on-line video data have grown dramatically and a large number of illegal copies have been appeared. To reduce any copyright infringement disputes, it is necessary to detect illegal copies on-line internet. Video fingerprint, which can express the video perceptual content as a compact description, is a key technology for copy detection. The video fingerprint algorithm based on spatio-temporal deep neural network is designed by the use of the excellent robustness of denoising auto-encoder (DAE) and building a deep neural network to extract features on frame level through greedily training DAE. Consequently, a long short-term memory network is adopted to extract each frame features of the deep network, and the training algorithm is designed on the basis of the theory of slow-feature analysis. Experimental results show that the proposed algorithm can reveal a high accuracy in video copy detection and outperform a number of the comparative algorithms.