Advertisement

An Open Access Platform for Analyzing Artistic Style Using Semantic Workflows

  • Ricky J. Sethi
  • Catherine A. Buell
  • William P. Seeley
  • Swaroop Krothapalli
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10966)

Abstract

We have created an open access online platform for using semantic workflows to analyze artistic style in paintings. We have implemented workflows for both standard computer vision image processing techniques and state-of-the-art methods such as convolutional neural networks to analyze images. These workflows can be used online by non-experts without needing any technical knowledge other than being able to use a browser.

We designed three artistically-relevant features to aid in the quantification of artistic style: the Discrete Tonal Measure, Discrete Variational Measure, and Convolutional Style Measure. These quantitative features can provide clues to the artistic elements that enable art scholars to categorize works as belonging to different artistic styles. We also created two new datasets of manually curated artworks selected especially for evaluating artistic style: one based on the school of art to which artists belong (Impressionism vs Hudson River) and one based on the medium used by a specific artist (tempera vs watercolors). Finally, we present an initial evaluation of these datasets and features for classifying paintings and also show results of a user study workshop for conducting such analyses online by humanities researchers, students, and professionals.

Keywords

Artistic style Visual stylometry Semantic workflows 

Notes

Acknowledgments

This research was supported in part by the US National Science Foundation (NSF) under grant #1019343 to the Computing Research Association for the CIFellows Project, the National Endowment for the Humanities (NEH) Grant under Award HD-248360-16, and the Amazon AWS Research Grant program (AMZN).

References

  1. 1.
    Bonnar, L., Gosselin, F., Schyns, P.G.: Understanding Dali’s slave market with the disappearing bust of voltaire: a case study in the scale information driving perception. Perception (2002).  https://doi.org/10.1068/p3276
  2. 2.
    Folego, G., Gomes, O., Rocha, A.: From impressionism to expressionism: automatically identifying van Gogh’s paintings. In: ICIP (2016)Google Scholar
  3. 3.
    Gatys, L.A., Ecker, A.S., Bethge, M.: A neural algorithm of artistic style. arXiv (2015)Google Scholar
  4. 4.
    Gil, Y.: Teaching big data analytics skills with intelligent workflow systems. In: Proceedings of the Sixth Symposium on Educational Advances in Artificial Intelligence (EAAI) (2016)Google Scholar
  5. 5.
    Goude, G., Derefeldt, G.: A study of Wolfflinś system for characterizing art. Stud. Art Educ. 22, 32–41 (1981)CrossRefGoogle Scholar
  6. 6.
    Graham, D.J., Hughes, J.M., Leder, H., Rockmore, D.N.: Statistics, vision, and the analysis of artistic style. Wiley Interdisc. Rev.: Comput. Stat. 4(2), 115–123 (2012).  https://doi.org/10.1002/wics.197CrossRefGoogle Scholar
  7. 7.
    Hauder, M., Gil, Y., Sethi, R.J., Liu, Y., Jo, H.: Making data analysis expertise broadly accessible through workflows. In: SC Works (2011)Google Scholar
  8. 8.
    Karayev, S., Trentacoste, M., Han, H., Agarwala, A., Darrell, T., Hertzmann, A., Winnemoeller, H.: Recognizing image style. BMVC, pp. 1–20 (2014).  https://doi.org/10.5244/C.28.122. http://arxiv.org/abs/1311.3715
  9. 9.
    Parker, D., Deregowski, J.: Perception and Artistic Style. Elsevier, New York City (1991). ISBN 9780444887023Google Scholar
  10. 10.
    Qi, H., Taeb, A., Hughes, S.M.: Visual stylometry using background selection and wavelet-HMT-based fisher information distances for attribution and dating of impressionist paintings. Sig. Process. 93(3), 541–553 (2013).  https://doi.org/10.1016/j.sigpro.2012.09.025CrossRefGoogle Scholar
  11. 11.
    Sethi, R.J., Gil, Y., Jo, H., Philpot, A.: Large-scale multimedia content analysis using scientific workflows. In: ACM International Conference on Multimedia (MM). ACM (2013)Google Scholar
  12. 12.
    Sowden, P.T., Schyns, P.G.: Channel surfing in the visual brain. Trends Cogn. Sci. 10(12), 538–545 (2006)CrossRefGoogle Scholar
  13. 13.
    Taylor, I., Deelman, E., Gannon, D., Shields, M. (eds.): Workflows for e-Science. Springer, London (2007).  https://doi.org/10.1007/978-1-84628-757-2CrossRefGoogle Scholar
  14. 14.
    Zhao, M., Zhu, S.C.: Abstract painting with interactive control of perceptual entropy. ACM Trans. Appl. Percept. 10(1), 1–21 (2013).  https://doi.org/10.1145/2422105.2422110MathSciNetCrossRefGoogle Scholar
  15. 15.
    Zujovic, J., Gandy, L., Friedman, S., Pardo, B., Pappas, T.N.: Classifying paintings by artistic genre: an analysis of features & classifiers. In: IEEE International Workshop on Multimedia Signal Processing (2009)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Ricky J. Sethi
    • 1
  • Catherine A. Buell
    • 1
  • William P. Seeley
    • 2
  • Swaroop Krothapalli
    • 1
    • 3
  1. 1.Fitchburg State UniversityFitchburgUSA
  2. 2.Boston CollegeBostonUSA
  3. 3.The Advisory BoardWashington, D.C.USA

Personalised recommendations