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ŠŮSTEK, M. VÍDEŇSKÝ, F. ZBOŘIL, F. ZBOŘIL, F.
Original Title
Family Coat of Arms and Armorial Achievement Classification
Type
conference paper
Language
English
Original 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
Authors
ŠŮSTEK, M.; VÍDEŇSKÝ, F.; ZBOŘIL, F.; ZBOŘIL, F.
Released
17. 4. 2019
Publisher
Springer International Publishing
Location
Los Alamitos
ISBN
2194-5357
Periodical
Advances in Intelligent Systems and Computing
Year of study
941
Number
2
State
Swiss Confederation
Pages from
577
Pages to
586
Pages count
9
URL
https://www.fit.vut.cz/research/publication/11848/
BibTex
@inproceedings{BUT156844, author="Martin {Šůstek} and František {Vídeňský} and František {Zbořil} and František {Zbořil}", title="Family Coat of Arms and Armorial Achievement Classification", booktitle="Intelligent Systems Design and Applications", year="2019", series="Advances in Intelligent Systems and Computing", journal="Advances in Intelligent Systems and Computing", volume="941", number="2", pages="577--586", publisher="Springer International Publishing", address="Los Alamitos", doi="10.1007/978-3-030-16660-1\{_}56", issn="2194-5357", url="https://www.fit.vut.cz/research/publication/11848/" }