@inproceedings{6f3dc3d8a4504643a6a471d4a8428c7c,
title = "Decomposing the video editing structure of a talk-show using nonnegative matrix factorization",
abstract = "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.",
keywords = "Video structuring, bag of features, indexing, machine learning, matrix factorization, unsupervised classification",
author = "S. Essid and C. F{\'e}votte",
year = "2012",
month = dec,
day = "1",
doi = "10.1109/ICIP.2012.6467557",
language = "English",
isbn = "9781467325332",
series = "Proceedings - International Conference on Image Processing, ICIP",
pages = "3105--3108",
booktitle = "2012 IEEE International Conference on Image Processing, ICIP 2012 - Proceedings",
note = "2012 19th IEEE International Conference on Image Processing, ICIP 2012 ; Conference date: 30-09-2012 Through 03-10-2012",
}