A video copy detection algorithm combining local feature's robustness and global feature's speed

This paper presents a novel algorithm for fast and robust video copy detection. The idea is to use local features to estimate the copy transformation parameters first and then use the estimated parameters to guide the global-feature-based matching at a later stage. It is based on the fact that the copy transformations generally remain unchanged in a continuous video clip even in the whole video. Local-feature-based matching can find the candidates which are difficult to be detected only using global features. Furthermore, the matched local feature points can provide enough information to estimate the copy transformations. After the copy transformations are estimated, the subsequent detection can be accelerated by doing global-feature-based matching. The experimental results show that the proposed algorithm can get the same good robustness as the local-feature-based method but the faster detection speed.