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A video "fingerprint" is a feature extracted from the video that should represent the video compactly, allowing faster search without compromising the retrieval accuracy. Here, we use a keyframe set to represent a video, motivated by the video summarization approach. We experiment with different features to represent each keyframe with the goal of identifying duplicate and similar videos. Various image processing operations like blurring, gamma correction, JPEG compression, and Gaussian noise addition are applied on the individual video frames to generate duplicate videos. Random and bursty frame drop errors of 20%, 40% and 60% (over the entire video) are also applied to create more noisy "duplicate" videos. The similar videos consist of videos with similar content but with varying camera angles, cuts, and idiosyncrasies that occur during successive retakes of a video. Among the feature sets used for comparison, for duplicate video detection, Compact Fourier-Mellin Transform (CFMT) performs the best while for similar video retrieval, Scale Invariant Feature Transform (SIFT) features are found to be better than comparable-dimension features. We also address the problem of retrieval of full-length videos with shorter-length clip queries. For identical feature size, CFMT performs the best for video retrieval.
Sarkar et al. (Fri,) studied this question.