Nowadays

content based video copy detection has become a widely studied issue. Due to the uncertainty and diversity of video copy transformation

it is difficult to achieve great performance based on single visual features in video copy detection. In this paper

we propose a new method

which uses a multiple visual feature synthesizing method to solve the problem. Besides traditional local and global visual features

we additionally employ nonorthogonal binary subspace(NBS) as special visual feature to represent the video content. On this basis

the normalized cross correlation(NCC) is used to improve the performance of similarity matching of the video content. We also used other measures to improve the detection accuracy. The experiment results show that our system is robust to various video transformations

and achieves better detection accuracy.