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Publication detail
SIKORA, P.
Original Title
Right convolutional neural network for classification illustrations in artworks
Type
article in a collection out of WoS and Scopus
Language
English
Original Abstract
This paper deals with the image classification problem in the field of artworks. The article uses a custom dataset from artworks with eight classes of some not common objects and illustrations. This dataset is used to train three convolutional neural networks for classification. All classification results are well discussed and evaluated with an example on the images from a dataset.
Keywords
artwork, convolutional neural network, deep learning, image classification, keras, machine learning
Authors
Released
27. 4. 2021
Publisher
Brno University of Technology, Faculty of Electrical Engineering and Comunnication
Location
Brno
ISBN
978-80-214-5942-7
Book
Proceedings of the 27th Conference STUDENT EEICT 2021
Edition
1
Pages from
591
Pages to
595
Pages count
5
URL
https://www.fekt.vut.cz/conf/EEICT/archiv/sborniky/EEICT_2021_sbornik_1.pdf
BibTex
@inproceedings{BUT172262, author="Pavel {Sikora}", title="Right convolutional neural network for classification illustrations in artworks", booktitle="Proceedings of the 27th Conference STUDENT EEICT 2021", year="2021", series="1", pages="591--595", publisher="Brno University of Technology, Faculty of Electrical Engineering and Comunnication", address="Brno", isbn="978-80-214-5942-7", url="https://www.fekt.vut.cz/conf/EEICT/archiv/sborniky/EEICT_2021_sbornik_1.pdf" }