Journal
NEURAL COMPUTING & APPLICATIONS
Volume 31, Issue 11, Pages 7361-7380Publisher
SPRINGER LONDON LTD
DOI: 10.1007/s00521-018-3579-x
Keywords
Video advertising; Deep learning; Object detection; Face verification; Image retrieval; Clothing detection
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Funding
- National Key R&D Program of China [2018YFB1003800]
- Natural Science Foundation of China [61572156]
- Shenzhen Science and Technology Program [JCYJ20170413105929681, JCYJ20170811161545863]
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This paper presents a novel learning-based framework for video content-based advertising, DeepLink, which aims at linking Sitcom-stars and online shops with clothing retrieval by using state-of-the-art deep convolutional neural networks (CNNs). Specifically, 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 crawled from real-world online shops. 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 clothes datasets. Extensive experimental results demonstrate the feasibility and efficacy of our proposed clothing-based video advertising system.
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