A fast and robust Key-frames based Video Copy Detection using BSIF-RMI

摘要

Content Based Video Copy Detection (CBVCD) has gained a lot of scientific interest in recent years. One of the biggest causes of video duplicates is transformation. This paper addresses a fast video copy detection approach based on key-frames extraction which is robust to different transformations. In the proposed scheme, the key-frames of videos are first extracted based on Gradient Magnitude Similarity Deviation (GMSD). The descriptor used in the detection process is extracted using a fusion of Binarized Statistical Image Features (BSIF) and Relative Mean Intensity (RMI). Feature vectors are then reduced by Principal Component Analysis (PCA), which can more accelerate the detection process while keeping a good robustness against different transformations. The proposed framework is tested on the query and reference dataset of CBCD task of Muscle VCD 2007 and TRECVID 2009. Our results are compared with those obtained by other works in the literature. The proposed approach shows promising performances in terms of both robustness and time execution.