Faculty of Information Technology, BUT

Publication Details

Family Coat of Arms and Armorial Achievement Classification

ŠŮSTEK Martin, VÍDEŇSKÝ František, ZBOŘIL František and ZBOŘIL František V.. Family Coat of Arms and Armorial Achievement Classification. In: Intelligent Systems Design and Applications. Advances in Intelligent Systems and Computing, vol 941, vol. 2019. Los Alamitos: Springer International Publishing, 2019, pp. 650-658. ISSN 2194-5357.
Czech title
Klasifikace rodových a úplných erbů
Type
conference paper
Language
english
Authors
Keywords
coats of arms, image classification, convolutional neural network, artificial intelligence, machine learning
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.
Annotation
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, 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 convnets 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.
Published
2019
Pages
650-658
Journal
Advances in Intelligent Systems and Computing, vol. 2019, no. 2, ISSN 2194-5357
Proceedings
Intelligent Systems Design and Applications
Series
Advances in Intelligent Systems and Computing, vol 941
Conference
18th International Conference on Intelligent Systems Designs and Applications, Vellore, IN
Publisher
Springer International Publishing
Place
Los Alamitos, US
DOI
BibTeX
@INPROCEEDINGS{FITPUB11848,
   author = "Martin \v{S}\r{u}stek and Franti\v{s}ek V\'{i}de\v{n}sk\'{y} and Franti\v{s}ek Zbo\v{r}il and V. Franti\v{s}ek Zbo\v{r}il",
   title = "Family Coat of Arms and Armorial Achievement Classification",
   pages = "650--658",
   booktitle = "Intelligent Systems Design and Applications",
   series = "Advances in Intelligent Systems and Computing, vol 941",
   journal = "Advances in Intelligent Systems and Computing",
   volume = 2019,
   number = 2,
   year = 2019,
   location = "Los Alamitos, US",
   publisher = "Springer International Publishing",
   ISSN = "2194-5357",
   doi = "10.1007/978-3-030-16660-1\_56",
   language = "english",
   url = "https://www.fit.vut.cz/research/publication/11848"
}
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