Laser & Optoelectronics Progress, Volume. 55, Issue 1, 11006(2018)
Video Fingerprint Algorithm Based on Spatio-Temporal Deep Neural Network
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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Wang Dongdong, Li Yuenan. Video Fingerprint Algorithm Based on Spatio-Temporal Deep Neural Network[J]. Laser & Optoelectronics Progress, 2018, 55(1): 11006
Category: Image Processing
Received: Jul. 18, 2017
Accepted: --
Published Online: Sep. 10, 2018
The Author Email: Dongdong Wang (wddtju@aliyun.com)