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research-article

Classification of Impressionist and Pointillist Paintings Based on Their Brushstrokes Characteristics

Published: 31 July 2024 Publication History

Abstract

The classification of works of art in terms of artistic style is a complex task. Some painting styles are closely related to the form of their brushstrokes. Salient examples are Pointillism and Impressionism, having both distinguishable brushstroke characteristics which are small, rounded of clear color, repetitive dots for Pointillism style and visible, elongated and slanting, repetitive touches for Impressionism style. As Impressionism is the ancestral style of Pointillism, the two styles have many elements in common and distinguishing them is difficult. In this article, specific texture features are investigated for the classification of the two styles, focusing mainly on small differences in their brushstrokes. The texture features adopted are Granulometric features, gray-level co-occurrence matrix features, and run length features. It is shown experimentally that the run length method outperforms the other features and can efficiently (up to 95%) discriminate the two textured styles since it incorporates information about size, direction, and intensity of brushstrokes.

References

[1]
T. Putri, R. Mukundan, and K. Neshatian. 2017. Artistic style characterization of Vincent Van Gogh’s paintings using extracted features from visible brush strokes. In International Conference on Pattern Recognition Applications and Methods.
[2]
A. Lecoutre, B. Négrevergne, and F. Yger. 2017. Recognizing art style automatically in painting with deep learning. In Ninth Asian Conference on Machine Learning.
[3]
Babak Saleh and Ahmed Elgammal. 2016. Large-scale classification of fine-art paintings: Learning the right metric on the right feature. International Journal for Digital Art History, 2 (October 2016). DOI:
[4]
K. Georgoulaki. 2022. Classification of Pointillist paintings using colour and texture features. International Journal of Electrical and Computer Engineering Research, 2, 1 (2022), 13–19. DOI:
[5]
Oguz Icoglu, B. Gunsel, and Sanem Sariel. 2004. Classification and indexing of paintings based on art movements. In 12th European Signal Processing Conference. 749–752.
[6]
Herbert Bay, Andreas Ess, Tinne Tuytelaars, and Luc Van Gool. 2008. Speeded-up robust features (SURF). Computer Vision and Image Understanding, 110, 3 (June 2008), 346–359. DOI:
[7]
María Díaz, Guillermo Ayala, Rafael Sebastian, and Llucía Martínez-Costa. 2007. Granulometric analysis of corneal endothelium specular images by using a germ-grain model. Computers in Biology and Medicine, 37 (2007), 364–375. DOI:
[8]
M. Khatun, A. Gray, and S. Marshall. 2011. Classification of ordered texture images using regression modelling and granulometric features. In 2011 Irish Machine Vision and Image Processing Conference. 64–69. DOI:
[9]
Robert Haralick, Kalaivani Shanmugam, and Ih Dinstein. 1973. Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, SMC-3 (1973), 610–621.
[10]
Lior Shamir and Jane A. Tarakhovsky. 2012. Computer analysis of art. Journal on Computing and Cultural Heritage, 5, 2, Article 7 (July 2012). DOI:
[11]
C. W. Almeida, R. M. Souza, and A. L. Candeias. 2010. Texture classification based on co-occurrence matrix and self-organizing map. In 2010 IEEE International Conference on Systems, Man and Cybernetics. 2487–2491.
[12]
M. M. Galloway. 1975. Texture analysis using gray level run lengths. Computer Graphics and Image Processing, 4 (1975), 172–179. DOI:
[13]
Xiaoou Tang. 1998. Texture information in run-length matrices. IEEE Transactions on Image Processing, 7 (1998), 1602–1609. DOI:
[14]
H. Zhang, C. L. Hung, G. Min, J.-P. Guo, M. Liu, and X. Hu. 2019. GPU-accelerated GLRLM algorithm for feature extraction of MRI. Scientific Reports, 9 (2019), 10883. DOI:
[15]
J. Koenderink, A. van Doorn, and K. Gegenfurtner. 2018. Area dominates edge in Pointillistic colour. Iperception, 9, 4 (2018), 2041669518788582. DOI:
[16]
M. Cartwright. 2022. Impressionism. World History Encyclopedia. Retrieved from https://www.worldhistory.org/Impressionism/
[17]
E. Cetinic, T. Lipić, and S. Grgic. 2018. Fine-tuning convolutional neural networks for fine art classification. Expert Systems with Applications, 114 (2018), 107–118.
[18]
A. I. Deac, J. van der Lubbe, and E. Backer. 2006. Feature selection for paintings classification by optimal tree pruning. In Multimedia Content Representation, Classification and Security. MRCS 2006. B. Gunsel, A. K. Jain, A. M. Tekalp, and B. Sankur (Eds.), Lecture Notes in Computer Science, Vol. 4105, Springer, Berlin. DOI:
[19]
WikiArt. 2024. Visual Art Encyclopedia. Retrieved from https://www.wikiart.org
[20]
Ivan Nunez-Garcia, Rocio A. Lizarraga-Morales, and Geovanni Hernandez-Gomez. 2018. Classification of paintings by artistic genre integrating color and texture descriptors. In International Conference on Artificial Intelligence and Pattern Recognition (AIPR ’18). Association for Computing Machinery, New York, NY, 66–70. DOI:
[21]
Y. Bar, N. Levy, and L. Wolf. 2015. Classification of artistic styles using binarized features derived from a deep neural network. In Computer Vision - ECCV 2014 Workshops. ECCV 2014. L. Agapito, M. Bronstein, and C. Rother (Eds.), Lecture Notes in Computer Science, Vol. 8925, Springer, Cham. DOI:
[22]
Roberto Ulloa. 2012. Recognition between Baroque and Renaissance Style of Classic Paintings. Technical Report. DOI:
[23]
N. Kayhan and S. Fekri-Ershad. 2021. Content based image retrieval based on weighted fusion of texture and color features derived from modified local binary patterns and local neighborhood difference patterns. Multimedia Tools and Applications, 80 (2021), 32763–32790. DOI:
[24]
M. K. Kelishadrokhi, M. Ghattaei, and S. Fekri-Ershad. 2023. Innovative local texture descriptor in joint of human-based color features for content-based image retrieval. SIViP, 17 (2023), 4009–4017. DOI:

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  1. Classification of Impressionist and Pointillist Paintings Based on Their Brushstrokes Characteristics

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    Published In

    cover image Journal on Computing and Cultural Heritage
    Journal on Computing and Cultural Heritage   Volume 17, Issue 3
    September 2024
    382 pages
    EISSN:1556-4711
    DOI:10.1145/3613582
    Issue’s Table of Contents

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 31 July 2024
    Online AM: 18 May 2024
    Accepted: 28 April 2024
    Revised: 11 March 2024
    Received: 02 September 2023
    Published in JOCCH Volume 17, Issue 3

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    1. Artistic styles
    2. image texture features
    3. painting style classification

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