Abstract
In this paper we propose a new method for automatic storyboard segmentation of TV news using image retrieval techniques and content manipulation. Our framework performs: shot boundary detection, global key-frame representation, image re-ranking based on neighborhood relations and temporal variance of image locations in order to construct a unimodal cluster for anchor person detection and differentiation. Finally, anchor shots are used to form video scenes. The entire technique is unsupervised being able to learn semantic models and extract natural patterns from the current video data. The experimental evaluation performed on a dataset of 50 videos, totalizing more than 30 h, demonstrates the pertinence of the proposed method, with gains in terms of recall and precision rates with more than 5–7% when compared with state of the art techniques.
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Acknowledgments
This work has been partially accomplished within the framework of the FUI 19 Media4D project, supported by BPI (Banque Publique d’investissement) France and DGE (Direction Generale des Entreprises).
This work was supported by a grant of the Romanian National Authority for Scientific Research and Innovation, CNCS - UEFISCDI, project number: PN-II-RU-TE-2014-4-0202.
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Mocanu, B., Tapu, R., Zaharia, T. (2016). Automatic Segmentation of TV News into Stories Using Visual and Temporal Information. In: Blanc-Talon, J., Distante, C., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2016. Lecture Notes in Computer Science(), vol 10016. Springer, Cham. https://doi.org/10.1007/978-3-319-48680-2_57
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DOI: https://doi.org/10.1007/978-3-319-48680-2_57
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