Automated curation of brand-related social media images with deep learning

  • Ruben Tous
  • Mauro Gomez
  • Jonatan Poveda
  • Leonel Cruz
  • Otto Wust
  • Mouna Makni
  • Eduard Ayguadé
Article
  • 37 Downloads

Abstract

This paper presents a work consisting in using deep convolutional neural networks (CNNs) to facilitate the curation of brand-related social media images. The final goal is to facilitate searching and discovering user-generated content (UGC) with potential value for digital marketing tasks. The images are captured in real time and automatically annotated with multiple CNNs. Some of the CNNs perform generic object recognition tasks while others perform what we call visual brand identity recognition. When appropriate, we also apply object detection, usually to discover images containing logos. We report experiments with 5 real brands in which more than 1 million real images were analyzed. In order to speed-up the training of custom CNNs we applied a transfer learning strategy. We examine the impact of different configurations and derive conclusions aiming to pave the way towards systematic and optimized methodologies for automatic UGC curation.

Keywords

Social media Instagram Twitter User generated content Deep learning Marketing 

Notes

Acknowledgements

This work is partially supported by the Spanish Ministry of Economy and Competitivity under contract TIN2015-65316-P and by the SGR programme (2014-SGR-1051) of the Catalan Government.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Universitat Politècnica de Catalunya (UPC)BarcelonaSpain
  2. 2.AdsmuraiBarcelonaSpain
  3. 3.Barcelona Supercomputing Center (BSC)BarcelonaSpain

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