Artistic Style Characterization of Vincent Van Gogh’s Paintings using Extracted Features from Visible Brush Strokes

Tieta Putri, Ramakrishnan Mukundan, Kourosh Neshatian

Abstract

This paper outlines important methods used for brush stroke region extraction for quantifying artistic style of Vincent Van Gogh’s paintings. After performing the region extraction, stroke-related features such as colour and texture features are extracted from the visible brush stroke regions. We then test the features by performing a binary classification between painters from different art movements and painters from the same art movement.

References

  1. Berezhnoy, I., Postma, E., and Herik, H. V. D. (2009). Automatic extraction of brushstroke orientation from paintings. In Machine Vision and Applications, volume 20, 1, pages 1-9.
  2. Bhargava, N., Sharma, G., Bhargava, R., and Mathuria, M. (2013). Decision tree analysis on j48 algorithm for data mining. In International Journal of Advanced Research in Computer Science and Software Engineering, volume 3, 6.
  3. Callen, A. (1982). Techniques of the Impressionists. QED Publishing/Chartwell Books.
  4. Gatys, L., Ecker, A., and Bethge, M. (2015). A neural algorithm of artistic style. In Nature Communications.
  5. Hughes, J., Graham, D., and Rockmore, D. (2010). Quantification of artistic style through sparse coding analysis in the drawings of pieter bruegel the elder. In Proceedings of the National Academy of Sciences, volume 107, 4, pages 1279-1283.
  6. Johnson., C. J., Hendriks, E., Berezhnoy, I., Brevdo, E., Hughes, S., Daubechies, I., Li, J., Postma, E., and Wang, J. (2008). Image processing for artist identification. InIEEE signal processing magazine, volume 25, 4, pages 37-48. IEEE.
  7. Li, J., Yao, L., Hendriks, E., and Wang, Y. (2012). Rhythmic brushstrokes distinguish van gogh from his contemporaries: findings via automated brushstroke extraction. In IEEE Trans. on Pattern Analysis and Machine Intelligence, volume 34, 6, pages 1159-1176. IEEE.
  8. Lombardi, T., Cha, S., and Tappert, C. (2004). A graphical user interface for a fine-art painting image retrieval system. In ACM SIGMM international workshop on Multimedia information retrieval, pages 107- 112. ACM.
  9. Lowe, D. (2004). Distinctive image features from scaleinvariant keypoints. In International Journal of Computer Vision, volume 60, 2, pages 91-110.
  10. Meer, P. and Georgescu, B. (2001). Edge detection with embedded confidence. In IEEE Trans. on Pattern Analysis and Machine Intelligence, volume 23, 12, pages 1351-1365. IEEE.
  11. Oliva, A. and Torralba, A. (2001). Modeling the shape of the scene: A holistic representation of the spatial envelope. In International Journal of Computer Vision, volume 42, 3, pages 145-175.
  12. Otsu, N. (1975). A threshold selection method from graylevel histograms. Automatica, 11(285-296):23-27.
  13. Putri, T. (2012). Non-photorealistic rendering of pointillistinspired images by an evolutionary process. Master's thesis, RMIT University.
  14. Putri, T. and Arymurthy, A. (2010). Image feature extraction and recognition of abstractionism and realism style of indonesian paintings. In IEEE Advances in Computing, Control and Telecommunication Technologies, pages 149-152. IEEE.
  15. Putri, T. and Mukundan, R. (2015). Iterative brush path extraction algorithm for aiding flock brush simulation of stroke-based painterly rendering. In International Conference on Evolutionary and Biologically Inspired Music and Art, pages 152-162. Springer.
  16. Reinhard, E., Khan, E., Akyuz, A., and Johnson, G. (2008). Colour Imaging: Fundamentals and Applications. AK Peters Ltd., Natick, MA.
  17. Rosseau, T. (1968). The stylistic detection of forgeries. In The Metropolitan Museum of Art Bulletin, volume 26, 6, pages 247-252. The Metropolitan Museum of Art.
  18. Sener, F., Samet, N., and Sahin, P. (2012). Identification of illustrators. In European Conference on Computer Vision, pages 589-597. Springer.
  19. Strassman, S. (1986). Hairy brush. In ACM Computer Graphics and Interactive Techniques Computer Graphics, volume 20, 4, pages 225-232.
  20. Su, M., Jean, W., and Chang, H. (1996). A static hand gesture recognition system using a composite neural network. In Fifth IEEE International Conference on Fuzzy Systems, pages 786-792. IEEE.
  21. Vieira, V., Fabbri, R., Sbrissa, D., da Fontoura Costa, L., and Travieso, G. (2015). A quantitative approach to painting styles. In Physica A: Statistical Mechanics and its Applications, volume 417, pages 110-129.
  22. Zang, Y., Huang, H., and Li, C. (2013). Stroke style analysis for painterly rendering. In Journal of Computer Science and Technology, volume 28, 5, pages 762-775. IEEE.
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Paper Citation


in Harvard Style

Putri T., Mukundan R. and Neshatian K. (2017). Artistic Style Characterization of Vincent Van Gogh’s Paintings using Extracted Features from Visible Brush Strokes . In Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-222-6, pages 378-385. DOI: 10.5220/0006188303780385


in Bibtex Style

@conference{icpram17,
author={Tieta Putri and Ramakrishnan Mukundan and Kourosh Neshatian},
title={Artistic Style Characterization of Vincent Van Gogh’s Paintings using Extracted Features from Visible Brush Strokes},
booktitle={Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2017},
pages={378-385},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006188303780385},
isbn={978-989-758-222-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Artistic Style Characterization of Vincent Van Gogh’s Paintings using Extracted Features from Visible Brush Strokes
SN - 978-989-758-222-6
AU - Putri T.
AU - Mukundan R.
AU - Neshatian K.
PY - 2017
SP - 378
EP - 385
DO - 10.5220/0006188303780385