Video replica detection utilizing R-trees and frame-based voting

A novel color-based two-step, coarse-to-fine video replica detection system is proposed in this paper. The first step uses an R-tree in order to perform a coarse selection of the database (original) videos that potentially match the query video. A training procedure that utilizes attacked versions of the database videos and aims at achieving robustness to attacks is being used. A frame-based voting procedure is also involved. A refinement step that processes the set of videos returned by the first step in order to select the final matching video (if any) follows. The performance of the system has been evaluated on a database of short videos with good results.