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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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References
Li, Y., et al.: Real time advertisement insertion in baseball video based on advertisement effect. In: Proceedings of ACMMM, pp. 343–346. ACM (2005)
Redondo, R.P.D., et al.: Bringing content awareness to web-based IDTV advertising. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.)42(3), 324–333 (2012)
Mei, T., et al.: VideoSense: a contextual in-video advertising system. IEEE Trans. Circuits Syst. Video Technol.19(12), 1866–1879 (2009)
Zhang, H., et al.: Object-level video advertising: an optimization framework. IEEE Trans. Ind. Inform.13(2), 520–531 (2017)
Yadati, K., Katti, H., Kankanhalli, M.: CAVVA: computational affective video-in-video advertising. IEEE Trans. Multimedia16(1), 15–23 (2014)
Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818–833. Springer, Cham (2014).https://doi.org/10.1007/978-3-319-10590-1_53
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014). arXiv preprintarXiv:1409.1556
He, K., et al.: Deep residual learning for image recognition. In: Proceedings of CVPR, pp. 770–778 (2016)
Krizhevsky, A., et al.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of CVPR, pp. 1–9 (2015)
Kiapour, M.H., et al.: Where to buy it: matching street clothing photos in online shops. In: Proceedings of ICCV, pp. 3343–3351 (2015)
Liu, X., et al.: Front. Comput. Sci. VIPLFaceNet: an open source deep face recognition SDK11, 208–218 (2017)
Lin, K., et al.: Deep learning of binary hash codes for fast image retrieval. In: Proceedings of CVPR Workshops, pp. 27–35 (2015)
Rodriguez, A., Laio, A.: Clustering by fast search and find of density peaks. Science344(6191), 1492–1496 (2014)
Ojala, T., et al.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell.24(7), 971–987 (2002)
Tan, X.Y., Triggs, B.: Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans. Image Process.19(6), 1635–1650 (2010)
Murala, S., et al.: Local tetra patterns: a new feature descriptor for content-based image retrieval. IEEE Trans. Image Process.21(5), 2874–2886 (2012)
Zhang, H., et al.: Organizing books and authors by multilayer SOM. IEEE Trans. Neural Netw. Learn. Syst.27(12), 2537–2550 (2016)
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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Authors and Affiliations
Department of Computer Science, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, China
Haijun Zhang, Yuzhu Ji, Wang Huang & Linlin Liu
- Haijun Zhang
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- Yuzhu Ji
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- Wang Huang
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- Linlin Liu
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Correspondence toYuzhu Ji.
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Editors and Affiliations
Simon Fraser University, Burnaby, BC, Canada
Jian Pei
Aristotle University of Thessaloniki, Thessaloniki, Greece
Yannis Manolopoulos
University of Queensland, Brisbane, QLD, Australia
Shazia Sadiq
University of Western Australia, Crawley, WA, Australia
Jianxin Li
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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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