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Family Coat of Arms and Armorial Achievement Classification

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Intelligent Systems Design and Applications (ISDA 2018 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 941))

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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.

M. Sustek and F. Vidensky—Contributed equally to this work.

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Notes

  1. 1.

    http://gis.fsv.cvut.cz/zamky/genealogie/harrach/php/info.php.

  2. 2.

    http://image-net.org/challenges/LSVRC/.

  3. 3.

    http://www.papirovehelmy.cz/zbozi/339-erb-rodu-bekovt.html http://www.boskovice.cz/znak-erb-a-prapor-mesta/d-22085.

  4. 4.

    http://www.image-net.org/.

  5. 5.

    http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf.

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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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Authors and Affiliations

  1. FIT, Brno University of Technology, IT4Innovations Centre of Excellence, Bozetechova 1/2, 612 66, Brno, Czech Republic

    Martin Sustek, Frantisek Vidensky, Frantisek Zboril Jr. & Frantisek V. Zboril

Authors
  1. Martin Sustek
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  2. Frantisek Vidensky
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  3. Frantisek Zboril Jr.
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  4. Frantisek V. Zboril
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Corresponding author

Correspondence to Frantisek Vidensky .

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Editors and Affiliations

  1. Machine Intelligence Research Labs, Auburn, WA, USA

    Ajith Abraham

  2. School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

    Aswani Kumar Cherukuri

  3. Tijuana Institute of Technology, Tijuana, Mexico

    Patricia Melin

  4. Machine Intelligence Research Labs, Auburn, WA, USA

    Niketa Gandhi

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Sustek, M., Vidensky, F., Zboril, F., Zboril, F.V. (2020). Family Coat of Arms and Armorial Achievement Classification. In: Abraham, A., Cherukuri, A., Melin, P., Gandhi, N. (eds) Intelligent Systems Design and Applications. ISDA 2018 2018. Advances in Intelligent Systems and Computing, vol 941. Springer, Cham. https://doi.org/10.1007/978-3-030-16660-1_56

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