Design and evaluation of an effective and efficient video copy detection system

We consider the end-to-end system design and evaluation of an efficient and effective system for video copy detection that bridges the gap between computationally expensive methods and practical applications. We use a compact SIFT-based bag-of-words fingerprint (which we call a SIFTogram), requiring only 1000 bytes per second of video, and show that beyond the descriptor choice, many variables can affect performance. We also consider a complementary color-based descriptor, which contrary to popular recent belief, performs better than SIFTogram on some transforms. We emphasize robustness with respect to the most common transformations on content sharing sites, and report a 99.3% detection rate with 0 false alarms on one such transform category from a standardized evaluation. We perform an evaluation of the system using two TRECVID benchmark datasets, and examine the trade-off between speed and accuracy relative to other TRECVID submissions.