Abstract
In this paper we introduce a novel advertisement system, dedicated to multimedia documents broadcasted over the Internet. The proposed approach takes into account the consumer’s perspective and inserts contextual relevant ads at the level of the scenes boundaries, while reducing the degree of intrusiveness. From the methodological point of view, the major contribution of the paper concerns a temporal video segmentation method into scenes based on a multimodal (visual, audio and semantic) fusion of information. The experimental evaluation, carried out on a large dataset with more than 30 video documents validates the proposed methodology with average F1 scores superior to 85%.
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Acknowledgement
This work has been carried out within the framework of the joint lab AITV (Artificial Intelligence for Television) established between Télécom SudParis and France Télévisions.
Part of this work was supported by a grant of the Romanian Ministery of Research and Innovation, CNCS – UEFISCDI, project number: PN-III-P1-1.1-TE-2019-0420, within PNCDI III.
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Mocanu, B., Tapu, R., Zaharia, T. (2020). A Multimodal High Level Video Segmentation for Content Targeted Online Advertising. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2020. Lecture Notes in Computer Science(), vol 12510. Springer, Cham. https://doi.org/10.1007/978-3-030-64559-5_40
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DOI: https://doi.org/10.1007/978-3-030-64559-5_40
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