The most popular approach for content-based video copy detection is based on bag-of-visual-words model with invariant local features. Due to the neglect of the spatial context information and the temporal context information

these methods are limited. An algorithm of representing the spatial and temporal context information of key frames quantified into binary codes

and spatial-temporal verification is quickly achieved by Hamming distance. Experiments on TREVID—2009 video set demonstrate the proposed algorithm has high efficiency and accuracy.