Fast copy detection based on Slice Entropy Scattergraph

With the exponential growth of digital video resources, huge amount of videos are uploaded onto the Internet. Therefore, the Content Based Copy Detection (CBCD) issue becomes a hot research topic and has been extensively studied recently. However, most of the approaches lack the power to efficiently handle large data corpus while maintaining a good detection quality. In this paper, we propose a fast CBCD approach based on the Slice Entropy Scattergraph (SES). SES employs video spatio-temporal slices which can greatly decrease the storage and computational complexity. It is based on entropy and its deviation so as to preserve as much as the video information. Besides, SES takes advantage of a scattergraph which is succinct and efficient to plot the distribution of video content. To effectively describe SES, we introduce three descriptors: Projection Histograms, Shape Contexts and Polynomial Coefficients. The experiments on CIVR´07 Copy Detection Corpus and Video Transformation Corpus show the performance improvement of our approach both on efficiency and effectiveness.