On efficient content-based near-duplicate video detection

The high usage of the Internet, in particular videosharing and social networking websites, have led to enormous amount of video data recently, raising demand on effective and efficient content-based near-duplicate video detection approaches. In this paper, we propose to efficiently search for near-duplicate videos via the utilization of efficient approximation techniques of the well-known effective similarity measure Earth Mover's Distance (EMD). To this end, we model keyframes by flexible feature representations which are then exploited in a filter-and-refine architecture to alleviate the query processing time. The experiments on real data indicate high efficiency guaranteeing reduced number of EMD computations, which contributes to the near-duplicate detection in video datasets.