Image Copy Detection Based on Convolutional Neural Networks

  • Jing Zhang
  • Wenting Zhu
  • Bing Li
  • Weiming Hu
  • Jinfeng Yang
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 663)

Abstract

In this paper, we present a model that automatically differentiates copied versions of original images. Unlike traditional image copy detection schemes, our system is a Convolutional Neural Networks (CNN) based model which means that it does not need any manually-designed features. In addition, a convolutional network is more applicable to image copy detection whose architecture is designed for robustness to geometric distortions. Our model uses fully connected layers to compute a similarity between CNN features, which are extracted from image pairs by a deep convolutional network. This method is very efficient and scalable to large databases. In order to see the comparison visually, a variety of models are explored. Experimental results demonstrate that our model presents surprising performance on various data sets.

Keywords

Image copy detection Feature extraction CNN 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2016

Authors and Affiliations

  • Jing Zhang
    • 1
  • Wenting Zhu
    • 2
  • Bing Li
    • 2
  • Weiming Hu
    • 2
  • Jinfeng Yang
    • 1
  1. 1.College of Electronic Information and AutomationCivil Aviation University of ChinaTianjinChina
  2. 2.National Laboratory of Pattern Recognition, CAS Center for Excellence in Brain Science and Intelligence TechnologyInstitute of Automation, Chinese Academy of SciencesBeijingChina

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