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Weakly Supervised Deep Metric Learning for Template Matching

  • Davit BuniatyanEmail author
  • Sergiy Popovych
  • Dodam Ih
  • Thomas Macrina
  • Jonathan Zung
  • H. Sebastian Seung
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 943)

Abstract

Template matching by normalized cross correlation (NCC) is widely used for finding image correspondences. NCCNet improves the robustness of this algorithm by transforming image features with siamese convolutional nets trained to maximize the contrast between NCC values of true and false matches. The main technical contribution is a weakly supervised learning algorithm for the training. Unlike fully supervised approaches to metric learning, the method can improve upon vanilla NCC without receiving locations of true matches during training. The improvement is quantified through patches of brain images from serial section electron microscopy. Relative to a parameter-tuned bandpass filter, siamese convolutional nets significantly reduce false matches. The improved accuracy of the method could be essential for connectomics, because emerging petascale datasets may require billions of template matches during assembly. Our method is also expected to generalize to other computer vision applications that use template matching to find image correspondences.

Keywords

Metric learning Weak supervision Siamese convolutional neural networks Normalized cross correlation 

Notes

Acknowledgment

This work has been supported by AWS Machine Learning Award and the Intelligence Advanced Research Projects Activity (IARPA) via Department of Interior/Interior Business Center (DoI/IBC) contract number D16PC0005. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. Disclaimer: The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of IARPA, DoI/IBC, or the U.S. Government.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Davit Buniatyan
    • 1
    Email author
  • Sergiy Popovych
    • 1
  • Dodam Ih
    • 1
  • Thomas Macrina
    • 1
  • Jonathan Zung
    • 1
  • H. Sebastian Seung
    • 1
  1. 1.Princeton UniversityPrincetonUSA

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