Compact CNN Based Video Representation for Efficient Video Copy Detection

Many content-based video copy detection (CCD) systems have been proposed to identify the copies of a copyrighted video. Due to storage cost and retrieval response requirements, most CCD systems represent video contents using sparsely sampled features, which tends to lose information to some extend and thus results in unsatisfactory performance. In this paper, we propose a compact video representation based on convolutional neural network (CNN) and sparse coding (SC) for video copy detection. We first extract CNN features from the densely sampled video frames and then encode them into a fixed length vector via the SC method. The proposed representation presents two advantages. First, it is compact while is regardless of the sampling frame rate. Second, it is discriminative for video copy detection by encoding the densely sampled frames’ CNN features. We evaluate the performance of proposed representation on video copy detection over a real complex video dataset and marginal performance improvement has been achieved as compared to state-of-the-art CCD systems.