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Emotions in Abstract Art: Does Texture Matter?

  • Andreza SartoriEmail author
  • Berhan Şenyazar
  • Alkim Almila Akdag Salah
  • Albert Ali Salah
  • Nicu Sebe
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9279)

Abstract

The classification of images based on the emotions they evoke is a recent approach in multimedia. With the abundance of digitized images from museum archives and the ever-growing digital production of user-generated images, there is a greater need for intelligent image retrieval algorithms. Categorization of images according to their emotional impact offers a useful addition to the state of the art in image search. In this work, we apply computer vision techniques on abstract paintings to automatically predict emotional valence based on texture. We also propose a method to derive a small set of features (Perlin parameters) from an image to represent its overall texture. Finally, we investigate the saliency distribution in these images, and show that computational models of bottom-up attention can be used to predict emotional valence in a parsimonious manner.

Keywords

Abstract paintings Emotion recognition Perlin images Saliency Eye-tracking 

References

  1. 1.
    Arnheim, R.: Art and Visual Perception: A Psychology of the Creative Eye. University of California Press (2004)Google Scholar
  2. 2.
    Bevins, J.: Libnoise library (2007). http://libnoise.sourceforge.net/ (accessed March 23, 2015)
  3. 3.
    Dirk, W., Koch, C.: Modeling attention to salient proto-objects. Neural Networks 19, 1395–1407 (2006)CrossRefzbMATHGoogle Scholar
  4. 4.
    Frintrop, S.: Computer Analysis of Human Behavior. In: Computational Visual Attention. Advances in Pattern Recognition. Springer (2011)Google Scholar
  5. 5.
    Herbrich, R., Graepel, T.: Trueskill(tm): A bayesian skill rating system. no. MSR-TR-2006-80 (2006)Google Scholar
  6. 6.
    Huang, G., Zhu, Q., Siew, C.: Extreme learning machine: a new learning scheme of feedforward neural networks. IJCNN. 2, 985–990 (2004)Google Scholar
  7. 7.
    Itten, J.: The Art of Color: The Subjective Experience and Objective Rationale of Color. Wiley (1974)Google Scholar
  8. 8.
    Kim, S., Kim, E.Y., Jeong, K.J., Kim, J.-I.: Emotion-based textile indexing using colors, texture and patterns. In: Bebis, G., et al. (eds.) ISVC 2006. LNCS, vol. 4292, pp. 9–18. Springer, Heidelberg (2006) CrossRefGoogle Scholar
  9. 9.
    Lang, P.J., Bradley, M.M., Cuthbert, B.N.: International affective picture system (iaps): Technical manual and affective ratings (1999)Google Scholar
  10. 10.
    Le Meur, O., Baccino, T.: Methods for comparing scanpaths and saliency maps: strengths and weaknesses. Behavior Research Methods 45(1), 251–266 (2012)CrossRefGoogle Scholar
  11. 11.
    Leder, H., Gerger, G., Dressler, S.G., Schabmann, A.: How art is appreciated. Psychology of Aesthetics, Creativity, and the Arts. 6(1) (2012)Google Scholar
  12. 12.
    Lucassen, M.P., Gevers, T., Gijsenij, A.: Texture affects color emotion. Color Research & Application 36(6), 426–436 (2011)CrossRefGoogle Scholar
  13. 13.
    Machajdik, J., Hanbury, A.: Affective image classification using features inspired by psychology and art theory. In: ACM Multimedia (2010)Google Scholar
  14. 14.
    Maji, S., Berg, A.C., Malik, J.: Classification using intersection kernel support vector machines is efficient. In: CVPR (2008)Google Scholar
  15. 15.
    Moholy-Nagy, L.. In Defense of “Abstract” Art. The Journal of Aesthetics and Art Criticism 4 (1945)Google Scholar
  16. 16.
    Moser, J.: True skill library (2010). https://github.com/moserware/Skills/ (accessed March 23, 2015)
  17. 17.
    Perlin, K.: An image synthesizer. ACM Siggraph Computer Graphics 19(3), 287–296 (1985)CrossRefGoogle Scholar
  18. 18.
    Sartori, A., Yanulevskaya, V., Salah, A., Uijlings, J., Bruni, E., Sebe, N.: Affective analysis of professional and amateur abstract paintings using statistical analysis and art theory. ACM Transactions on Interactive Intelligent Systems (TiiS), in press (2015)Google Scholar
  19. 19.
    Simmons, D.R., Russell, C.: Visual texture affects the perceived unpleasantness of colours. Perception 37, 146–146 (2008)Google Scholar
  20. 20.
    Thumfart, S., Jacobs, R.H., Lughofer, E., Eitzinger, C., Cornelissen, F.W., Groissboeck, W., Richter, R.: Modeling human aesthetic perception of visual textures. ACM Transactions on Applied Perception (TAP) 8(4), 27 (2011)Google Scholar
  21. 21.
    Varma, M., Zisserman, A.: A statistical approach to texture classification from single images. IJCV 62(1–2), 61–81 (2005)CrossRefGoogle Scholar
  22. 22.
    Yanulevskaya, V., Gemert, J.V., Roth, K., Herbold, A., Sebe, N., Geusebroek, J.: Emotional valence categorization using holistic image features. In: ICIP (2008)Google Scholar
  23. 23.
    Yanulevskaya, V., Uijlings, J., Bruni, E., Sartori, A., Zamboni, E., Bacci, F., Melcher, D., Sebe, N.: In the eye of the beholder: employing statistical analysis and eye tracking for analyzing abstract paintings. In: ACM Multimedia (2012)Google Scholar
  24. 24.
    Zhang, H., Yang, Z., Gönen, M., Koskela, M., Laaksonen, J., Honkela, T., Oja, E.: Affective abstract image classification and retrieval using multiple kernel learning. In: Lee, M., Hirose, A., Hou, Z.-G., Kil, R.M. (eds.) ICONIP 2013, Part III. LNCS, vol. 8228, pp. 166–175. Springer, Heidelberg (2013) CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Andreza Sartori
    • 1
    • 2
    Email author
  • Berhan Şenyazar
    • 3
  • Alkim Almila Akdag Salah
    • 4
  • Albert Ali Salah
    • 3
  • Nicu Sebe
    • 1
  1. 1.DISIUniversity of TrentoTrentoItaly
  2. 2.SKIL LabTelecom ItaliaTrentoItaly
  3. 3.Bogazici UniversityIstanbulTurkey
  4. 4.University of AmsterdamAmsterdamThe Netherlands

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