Video Copy Detection Using Spatio-Temporal CNN Features

Abstract

To protect the copyright of digital videos, video copy detection has become a hot topic in the field of digital copyright protection. Since a video sequence generally contains a large amount of data, to achieve efficient and effective copy detection, the key issue is to extract compact and discriminative video features. To this end, we propose a video copy detection scheme using spatio-temporal convolutional neural network (CNN) features. First, we divide each video sequence into multiple video clips and sample the frames of each video clip. Second, the sampled frames of each video clip are fed into a pre-trained CNN model to generate the corresponding convolutional feature maps (CFMs). Third, based on the generated CFMs, we extract the CNN features on the spatial and temporal domains of each video clip, i.e., the spatio-temporal CNN features. Finally, video copy detection is efficiently and effectively implemented based on the extracted spatio-temporal CNN features. The experiments on the commonly used video dataset, i.e., TRECVID 2008, demonstrate that the proposed method performs well in aspects of both accuracy and efficiency and shows superiority to several other copy detection methods using the state-of-the-art features.

Keywords

• Video copy detection
• video security
• copyright protection
• convolutional neural network (CNN)
• convolutional feature maps
• spatio-temporal CNN features