Fast and accurate content-based video copy detection using bag-of-global visual features

In this paper, we propose a fast, accurate content-based video copy detection scheme based on bag-of-global visual features, which is characterized by (1) utilizing an efficient DCT-sign-based feature for fast detection; (2) performing multiple assignment in the temporal domain, in addition to the feature and spatial domain to ensure repeatability in segment-level matching; and (3) adopting an inverse document frequency weighting and temporal burstiness-aware scoring to emphasize distinctive visual words. Despite detection 95 times faster than real-time, the proposed system achieves a false negative rate of 0.2% against queries that are altered by non-geometric transformations without any false positives.