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An effective video copy detection framework should be robust against spatial and temporal variations, e.g., changes in brightness and speed. To this end, a content-based approach for video copy detection is proposed. We define the problem as a partial matching problem in a probabilistic model and transform it into a shortest-path problem in a matching graph. To reduce the computation costs of the proposed framework, we introduce some methods that rapidly select key frames and candidate segments from a large amount of video data. The experiment results show that the proposed approach not only handles spatial and temporal variations well, but it also reduces the computation costs substantially.