A quick video search method based on local and global feature clustering

This paper proposes a quick method of similarity-based video searching to detect and locate a specific video clip given as a query in a stored long video stream. The method employs a two-stage process: local and global feature clustering. The local clustering exploits continuity or local similarities between video features, and the global clustering gathers similar video frames that are not necessarily adjacent to each other. These processes prune irrelevant sections on a video stream. The method guarantees the exactly same search result as the exhaustive search. Experiments performed on a PC show that the proposed method can correctly detect and locate a 7.5-second clip in a 150-hour video recording in 15 ms on average.