On efficient content-based near-duplicate video detection

Merih Seran Uysal, Christian Beecks, Thomas Seidl

Abstract: 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.