  Content-based near-duplicate video detection (NDVD) is essential for
effective search and retrieval, and robust video fingerprinting is a good
solution for NDVD. Most existing video fingerprinting methods use a single
feature or concatenating different features to generate video fingerprints, and
show a good performance under single-mode modifications such as noise addition
and blurring. However, when they suffer combined modifications, the performance
is degraded to a certain extent because such features cannot characterize the
video content completely. By contrast, the assistance and consensus among
different features can improve the performance of video fingerprinting.
Therefore, in the present study, we mine the assistance and consensus among
different features based on tensor model, and present a new comprehensive
feature to fully use them in the proposed video fingerprinting framework. We
also analyze what the comprehensive feature really is for representing the
original video. In this framework, the video is initially set as a high-order
tensor that consists of different features, and the video tensor is decomposed
via the Tucker model with a solution that determines the number of components.
Subsequently, the comprehensive feature is generated by the low-order tensor
obtained from tensor decomposition. Finally, the video fingerprint is computed
using this feature. A matching strategy used for narrowing the search is also
proposed based on the core tensor. The robust video fingerprinting framework is
resistant not only to single-mode modifications, but also to the combination of
them.
