Abstract
This paper introduces a novel learning-based framework for video content-based advertising, DeepLink, which aims at linking sitcom-stars and online stores with clothing retrieval by using state-of-the-art deep convolutional neural networks (CNNs). Concretely, several deep CNN models are adopted for composing multiple sub-modules in DeepLink, including human-body detection, human-pose selection, face verification, clothing detection and retrieval from advertisements (ads) pool that is constructed by clothing images collected from real-world online stores. For clothing detection and retrieval from ad images, we firstly transfer the state-of-the-art deep CNN models to our data domain, and then train corresponding models based on our constructed large-scale clothing datasets. Extensive experimental results demonstrate the feasibility and efficacy of our proposed clothing-based video-advertising system.
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Acknowledgments
This work was supported in part by the Natural Science Foundation of China under Grant 61572156 and in part by the Shenzhen Science and Technology Program under Grant JCYJ20170413105929681.
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Zhang, H., Ji, Y., Huang, W., Liu, L. (2018). Sitcom-Stars Oriented Video Advertising via Clothing Retrieval. In: Pei, J., Manolopoulos, Y., Sadiq, S., Li, J. (eds) Database Systems for Advanced Applications. DASFAA 2018. Lecture Notes in Computer Science(), vol 10828. Springer, Cham. https://doi.org/10.1007/978-3-319-91458-9_39
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DOI: https://doi.org/10.1007/978-3-319-91458-9_39
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