Decomposing the video editing structure of a talk-show using nonnegative matrix factorization

We introduce a novel video structuring scheme that exploits nonnegative matrix factorization (NMF) on count data (in a bag of features representation of the visual stream) to jointly discover latent structuring patterns and their activations in time. Our NMF variant employs the Kullback-Leibler divergence as a cost function and imposes a temporal smoothness constraint to the activations. It is solved by a majorization-minimization technique. Our method is shown to be successful for decomposing the high-level editing structure of talk-shows. It is evaluated using a challenging database of TV political-debate programs, and found to clearly outperform a reference HMM method.