Speeding Up Complex Video Copy Detection Queries

Ira Assent, Hardy Kremer, Thomas Seidl

Abstract: Massive amounts of video data from digital tv channels, online video communities, peer-to-peer networks, and video blogs require automated techniques for copyright enforcement and usage tracking. Effective video copy distortion models usually incur high computational cost. We propose an index supported multistep filter-and-refine algorithm for a complex copy detection model. We characterize a class of filters for which we prove completeness of the result, and provide further runtime improvement by a novel tight approximation. In thorough experiments, we demonstrate that our algorithm substantially improves processing times.