Softcomputing system for painting art style verification

• Autorzy: Mazurkiewicz, Jacek
• Konferencja: 17th Summer Safety & Reliability Seminars - SSARS 2023, 9-14 July 2023, Kraków, Poland
• Języki publikacji: EN

Abstrakty

EN

The chapter discusses the foundations for the system to verify and recognize the art style. Such a system seems to be interesting for the first step of the painting fraud identification and following the possible path of the different style influence for the final shape of the masterpiece. The approach presents image recognition using convolutional neural networks. These networks, due to their structure resembling the sight apparatus, and due to their efficiency in the case of two-dimensional data, are very often used for image recognition. Style recognition in art is currently a hot topic in machine learning circles. Courtesy of museums and galleries, there are now many databases available on the web that can be used in scientific work. The classes on which the network was to be tested are styles in history that are associated by the layman with the subject of art. This group includes Renaissance, Baroque, Romanticism, Neoclassicism, Surrealism, Cubism, Art Nouveau, Abstract Expressionism, Pop Art, and Impressionism. These classes were described in the work in terms of parameters that could affect the learning of the neural network. The networks were tested to determine the best parameters for identifying artistic styles. Networks with changing filter values, stride, and pooling parameters, and by selecting various additional layers were tested. The most important parameter was overfitting, which had to be prevented. As a result, networks peaked at 40% in Top1 and 80% in Top3. For smaller data, this result was optimistic for further research in recognizing other parameters, as well as using networks that were previously taught specific characteristics of styles, such as frequently used motifs or colors.

Słowa kluczowe

EN

art style recognition; convolution neural network; deep learning; image recognition

• Czasopismo: Safety and Reliability of Systems and Processes
• Rocznik: 2023
• Tom: Summer Safety and Reliability Seminar 2023
• Strony: 157--174
• Opis fizyczny: Bibliogr. 21 poz., rys., tab.

Twórcy

autor

Mazurkiewicz, Jacek

• Wrocław University of Science and Technology, Wrocław, Poland,

Bibliografia

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• Bar, Y., Levy, N., Wolf, L. 2015. Classification of Artistic Styles Using Binarized Features Derived from a Deep Neural Network. http://pdfs.semanticscholar.org/2b20/33af5ae4e705b90e970a586e0431678374b2.pdf (accessed 4 June 2023).
• Blessing, A., Wen, K. 2018. Using Machine Learning for Identification of Art Paintings. https://pdfs.semanticscholar.org/1d73/0a452a5c03cc23f90d4fde71c08864f31c35.pdf (accessed 30 May 2023).
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• Pai, P. 2017. Data Augmentation Techniques in CNN using TensorFlow, https://medium.com/ymedialabs-innovation/data-augmentation-techniques-in-cnn-using-TensorFlow-371ae43d5be9 (accessed 10 June 2023).
• 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, 2016. E. Scikit-learn: Machine Learning in Python, http://scikit-learn.org/ (accessed 10 June 2023).
• Shamir, L., Tarakhovsky. J.A. 2012. Computer analysis of art. Journal on Computing and Cultural Heritage 5(2), 1-11.
• Singh, V. 2017. Convolutional Neural Network for Image Classification, www.completegate.com/2017022864/blog/deep-machine-learning-images-lenet-alexnet-cnn (accessed 28 May 2023).
• Viswanathan, N. 2017. Artist Identification with Convolutional Neural Networks. Stanford University, USA.
• Zaki, F., 2017. Identify This Art, http://www.identifythisart.com/ (accessed 20 April 2023).

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).

Identyfikatory

DOI

10.26408/srsp-2023-12