Abstract
Recognition of art style in painting is an active and important research area in the computer visions field as art style recognition is not depending on the definite feature; however, extracting important in-definitive features from a painting image is a crucial fact. Moreover, using deep convolutional neural network (CNN) makes the model overfit and small CNN model makes the model underfit. To overcome these problems, in this work, a model is developed by us for art style recognition in painting based on the shallow convolutional neural network (SCNN). The proposed model is developed based on the two consecutive convolutional layers and single fully connected layer to extract painting features and recognize the art styles accordingly. For training and testing purposes in the art style recognition, we have used Wikipaintings dataset. We also used our created real-time dataset in this work. Our proposed model shows 60.37% accuracy which is the significant improvement over some current state of the arts.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
W.R. Tan, C.S. Chan, H.E. Aguirre, K. Tanaka, Ceci n’est pas une pipe: a deep convolutional network for fine-art paintings classification, in 2016 IEEE International Conference on Image Processing (ICIP) (IEEE, 2016), pp. 3703–3707
Y. LeCun, K. Kavukcuoglu, C. Farabet, Convolutional networks and applications in vision, in IEEE International Symposium on Circuits and Systems, pp. 253–256 (2010)
A. Binder, W. Samek, G. Montavon, S. Bach, K.R. Müller, Analyzing and validating neural networks predictions, in Proceedings of the ICML 2016 Workshop on Visualization for Deep Learning (2016)
S. Karayev, M. Trentacoste, H. Han, A. Agarwala, T. Darrell, A. Hertzmann, H. Winnemoeller, Recognizing image style. arXiv preprint arXiv:1311.3715 (2013)
A. Krizhevsky, I. Sutskever, G.E. Hinton, Imagenet classification with deep convolutional neural networks. Adv. Neural. Inf. Process. Syst. 25, 1097–1105 (2012)
M.O. Kelek, N. Calik, T. Yildirim, Painter classification over the novel art painting data set via the latest deep neural networks. Proc. Comput. Sci. 154, 369–376 (2019)
G. Liu, Y. Yan, E. Ricci, Y. Yang, Y. Han, S. Winkler, N. Sebe, Inferring painting style with multi-task dictionary learning, in IJCAI International Joint Conference on Artificial Intelligence (2015)
O.E. David, N.S. Netanyahu, Deeppainter: Painter classification using deep convolutional autoencoders, in International Conference on Artificial Neural Networks (Springer, Cham, 2016), pp. 20–28
Y. Hong, J. Kim, Art painting identification using convolutional neural network. Int. J. Appl. Eng. Res. 12(4), 532–539 (2017)
Y. Bar, N. Levy, L. Wolf, Classification of artistic styles using binarized features derived from a deep neural network, in European Conference on Computer Vision (Springer, Cham, 2014), pp. 71–84
B. Saleh, A. Elgammal, Large-scale classification of fine-art paintings: learning the right metric on the right feature. arXiv preprint arXiv:1505.00855 (2015)
L.A. Gatys, A.S. Ecker, M. Bethge, A neural algorithm of artistic style. arXiv preprint arXiv:1508.06576 (2015)
K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
A. Lecoutre, B. Negrevergne, F. Yger, Recognizing art style automatically in painting with deep learning, in Asian Conference on Machine Learning (PMLR, 2017), pp. 327–342
A. Ismail, S.A. Ahmad, A.C. Soh, K. Hassan, H.H. Harith, Improving convolutional neural network (CNN) architecture (miniVGGNet) with batch normalization and learning rate decay factor for image classification. Int. J. Integr. Eng. 11(4) (2019)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Akter, M., Akther, M.R., Khaliluzzaman, M. (2022). Recognizing Art Style Automatically in Painting Using Convolutional Neural Network. In: Kumar, A., Zurada, J.M., Gunjan, V.K., Balasubramanian, R. (eds) Computational Intelligence in Machine Learning. Lecture Notes in Electrical Engineering, vol 834. Springer, Singapore. https://doi.org/10.1007/978-981-16-8484-5_20
Download citation
DOI: https://doi.org/10.1007/978-981-16-8484-5_20
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-8483-8
Online ISBN: 978-981-16-8484-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)