Real-time large scale near-duplicate web video retrieval

Near-duplicate video retrieval is becoming more and more important with the exponential growth of the Web. Though various approaches have been proposed to address this problem, they are mainly focusing on the retrieval accuracy while infeasible to query on Web scale video database in real time. This paper proposes a novel method to address the efficiency and scalability issues for near-duplicate We video retrieval. We introduce a compact spatiotemporal feature to represent videos and construct an efficient data structure to index the feature to achieve real-time retrieving performance. This novel feature leverages relative gray-level intensity distribution within a frame and temporal structure of videos along frame sequence. The new index structure is proposed based on inverted file to allow for fast histogram intersection computation between videos. To demonstrate the effectiveness and efficiency of the proposed methods we evaluate its performance on an open Web video data set containing about 10K videos and compare it with four existing methods in terms of precision and time complexity. We also test our method on a data set containing about 50K videos and 11M key-frames. It takes on average 17ms to execute a query against the whole 50K Web video data set.

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