Sitcom-star-based clothing retrieval for video advertising: a deep learning framework

Sitcom-star-based clothing retrieval for video advertising: a deep learning framework 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. Keywords Video advertising  Deep learning  Object detection  Face verification  Image retrieval  Clothing detection 1 Introduction advertisers, undifferentiated advertising for all of the users will increase the advertising cost and waste resources Recent years have witnessed dramatic development of the which, to some extent, reduces advertising efficiency. Internet economy. According to the increasing online video Thus, a tradeoff between reducing the impact on users traffic and its growing revenue, video advertising offers viewing experience and keeping the advertising http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Neural Computing and Applications Springer Journals

Sitcom-star-based clothing retrieval for video advertising: a deep learning framework

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Publisher
Springer Journals
Copyright
Copyright © 2018 by The Natural Computing Applications Forum
Subject
Computer Science; Artificial Intelligence (incl. Robotics); Data Mining and Knowledge Discovery; Probability and Statistics in Computer Science; Computational Science and Engineering; Image Processing and Computer Vision; Computational Biology/Bioinformatics
ISSN
0941-0643
eISSN
1433-3058
D.O.I.
10.1007/s00521-018-3579-x
Publisher site
See Article on Publisher Site

Abstract

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. Keywords Video advertising  Deep learning  Object detection  Face verification  Image retrieval  Clothing detection 1 Introduction advertisers, undifferentiated advertising for all of the users will increase the advertising cost and waste resources Recent years have witnessed dramatic development of the which, to some extent, reduces advertising efficiency. Internet economy. According to the increasing online video Thus, a tradeoff between reducing the impact on users traffic and its growing revenue, video advertising offers viewing experience and keeping the advertising

Journal

Neural Computing and ApplicationsSpringer Journals

Published: Jun 7, 2018

References

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