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Family Coat of Arms and Armorial Achievement Classification

  • Martin Sustek
  • Frantisek VidenskyEmail author
  • Frantisek ZborilJr.
  • Frantisek V. Zboril
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 941)

Abstract

This paper presents an approach to classification of family coats of arms and armorial achievement. It is difficult to obtain images with coats of arms because not many of them are publicly available. To the best of our knowledge, there is no dataset. Therefore, we artificially extend our dataset using Neural Style Transfer technique and simple image transformations. We describe our dataset and the division into training and test sets that respects the lack of data examples. We discuss results obtained with both small convolutional neural network (convnet) trained from scratch and modified architectures of various convents pretrained on Imagenet dataset. This paper further focuses on the VGG architecture which produces the best accuracy. We show accuracy progress during training, per-class accuracy and a normalized confusion matrix for VGG16 architecture. We reach top-1 accuracy of nearly 60% and top-5 accuracy of 80%. To the best of our knowledge, this is the first family coats of arms classification work, so we cannot compare our results with others.

Keywords

Coats of arms Image classification Convolutional neural network Artificial intelligence Machine learning 

Notes

Acknowledgment

This work was supported by the BUT project FIT-S-17-4014 and the IT4IXS: IT4Innovations Excellence in Science project (LQ1602).

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Martin Sustek
    • 1
  • Frantisek Vidensky
    • 1
    Email author
  • Frantisek ZborilJr.
    • 1
  • Frantisek V. Zboril
    • 1
  1. 1.FITBrno University of Technology, IT4Innovations Centre of ExcellenceBrnoCzech Republic

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