Publication detail

Right convolutional neural network for classification illustrations in artworks

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

SIKORA, P.

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"
}