Scalable mining of large video databases using copy detection

Mining the video content itself can bring to light important information regarding the internal structure of large video databases, compensating for a lasting absence of extensive and reliable annotations. Many valuable links between video segments can be identified by content-based copy detection methods, where "copies" are transformed versions of original video sequences. To make this approach viable for large video databases, we put forward a new mining method relying on the definition of a compact keyframe-level descriptor and of a specific index structure. The performance obtained in detecting links between video segments is evaluated with the help of a ground truth and several illustrations are given. The scalability of the approach is then demonstrated for databases of up to 10,000 hours of video.