Modeling temporal correlations in content fingerprints

Previous analysis of content fingerprints has mainly focused on the case of independent and identically distributed fingerprints. Practical fingerprints, however, exhibit correlations between components computed from successive frames. In this paper, a Markov chain based model is used to capture the temporal correlations, and the suitability of this model is evaluated through experiments on a video database. The results indicate that the Markov chain model is a good fit only in a certain regime. A hybrid model is then developed to account for this behavior and a corresponding adaptive detector is derived. The adaptive detector achieves better identification accuracy at a small computational expense.