Abstract
The paper discuss the possibility to use the softcomputing methods to create effective and useful system for art style identification. The system should operate on the samples of paintings. The assumption is to use only the small parts of pictures with no high resolution. Different types of preprocessing methods are tested to create the input vectors for Convolutional Neural Network (CNN) which is an identification tool. The experiments are done for the significant dataset covering ten most classic art styles of paintings. Different types of CNN topology is discussed. The promising results could be an interesting subject for custodians, art historians or scientists. This may help them not only recognize the style with some certainty but also compare and mark the similarities and differences between styles or artists. The paper can be extended to help them in authenticating and determining the timeline of paintings.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bar, Y., Levy, N., Wolf, L.: Classification of artistic styles using Binarized features derived from a deep neural network (2015). http://pdfs.semanticscholar.org/2b20/33af5ae4e705b90e970a586e0431678374b2.pdf. Accessed June 2018
Blessing, A., Wen, K.: Using machine learning for identification of art paintings. https://pdfs.semanticscholar.org/1d73/0a452a5c03cc23f90d4fde71c08864f31c35.pdf. Accessed May 2018
Google Arts and Culture. artsandculture.google.com
Hearty, J.: Advanced Machine Learning with Python. Packt Publishing, Birmingham (2016)
Hockney, D., Gayford, M.: History of Pictures: From the Cave to the Computer Screen. Thames & Hudson Ltd., London (2016)
Krizvsky, A., Skutskever, I., Hinton, G.: ImageNet Classification with Deep Convolutional Neural Networks. https://www.nvidia.cn/content/tesla/pdf/machine-learning/imagenet-classification-with-deep-convolutional-nn.pdf. Accessed June 2018
Lecountre, A., Negrevergne, B., Yger, F.: Recognizing art style automatically in painting with deep learning, France (2017). http://www.lamsade.dauphine.fr/~bnegrevergne/webpage/documents/2017_rasta.pdf. Accessed Mar 2018
Nielsen, M.: Neural Network and Deep Learning. Determination Press (2015). http://neuralnetworksanddeeplearning.com/. Accessed Apr 2018
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: machine learning in Python 2011. http://scikit-learn.org/. Accessed Apr 2018
Singh, V.: Convolutional Neural Network for Image Classification (2017). www.completegate.com/2017022864/blog/deep-machine-learning-images-lenet-alexnet-cnn. Accessed May 2018
Zaki, F.: Identify This Art (2015). http://www.identifythisart.com/. Accessed Apr 2018
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Mazurkiewicz, J., Cybulska, A. (2020). Softcomputing Art Style Identification System. In: Zamojski, W., Mazurkiewicz, J., Sugier, J., Walkowiak, T., Kacprzyk, J. (eds) Engineering in Dependability of Computer Systems and Networks. DepCoS-RELCOMEX 2019. Advances in Intelligent Systems and Computing, vol 987. Springer, Cham. https://doi.org/10.1007/978-3-030-19501-4_32
Download citation
DOI: https://doi.org/10.1007/978-3-030-19501-4_32
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-19500-7
Online ISBN: 978-3-030-19501-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)