Effective Video Copy Detection Using Statistics of Quantized Zernike Moments

Video copy detection has found wide applications in digital multimedia forensics and copyright protection. With video copy detection, one can not only determine the presence of a query video in the massive video database, but also locate it precisely. This paper presents an effective video copy detection scheme based on the statistics of quantized Zernike moments. In our approach, each video frame is partitioned into non-overlapping blocks. The Zernike moments of first few orders are then calculated for each block. Finally, the frame-level feature is generated by aggregating statistics of the quantized Zernike moments of all the blocks in the video frame. Through extensive experiments on a public video database, this frame-level feature is demonstrated to be robust against geometric transformation, color adjustment, noise contamination and many other commonly used content-preserving operations. Compared with existing schemes in the literatures, the proposed method yields better or at least comparable performance in a series of experiments.