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  • Received: Jul. 18, 2017

    Accepted: --

    Posted: Sep. 10, 2018

    Published Online: Sep. 10, 2018

    The Author Email: Wang Dongdong (wddtju@aliyun.com), Li Yuenan (ynli@tju.edu.cn)

    DOI: 10.3788/LOP55.011006

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

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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

Abstract

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.

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