An Exploration of Visual Complexity

  • Helen C. Purchase
  • Euan Freeman
  • John Hamer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7352)

Abstract

Inspired by the contrast between ‘classical’ and ‘expressive’ visual aesthetic design, this paper explores the ‘visual complexity’ of images. We wished to investigate whether the visual complexity of an image could be quantified so that it matched participants’ view of complexity. An empirical study was conducted to collect data on the human view of the complexity of a set of images. The results were then related to a set of computational metrics applied to these images, so as to identify which objective metrics best encapsulate the human subjective opinion. We conclude that the subjective notion of ‘complexity’ is consistent both to an individual and to a group, but that it does not easily relate to the most obvious computational metrics.

Keywords

Image complexity visual aesthetic image processing empirical results 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Salimun, C., et al.: The effect of aesthetically pleasing composition on visual search performance. In: Nordic Human Computer Interaction Conference, pp. 422–431. ACM (2010)Google Scholar
  2. 2.
    Hartmann, J., Sutcliffe, A., De Angeli, A.: Investigating attractiveness in web user interfaces. In: Human Factors in Computing Systems (CHI) Conference, pp. 387–396 (2007)Google Scholar
  3. 3.
    Hassenzahl, M.: The Interplay of Beauty, Goodness, and Usability in Interactive Products. Human-Computer Interaction 19, 319–349 (2004)CrossRefGoogle Scholar
  4. 4.
    Kurosu, M., Kashimura, K.: Apparent usability vs inherent usability: experimental analysis on the determinants of the apparent usability. In: Human Factors in Computing Systems (CHI) Conference (1995)Google Scholar
  5. 5.
    Hartmann, J., Sutcliffe, A., De Angeli, A.: Towards a Theory of User Judgment of Aesthetics and User Interface Quality. ACM Transactions on Computer-Human Interaction 15(4), 15 (2008)CrossRefGoogle Scholar
  6. 6.
    Lavie, T., Tractinsky, N.: Assessing dimensions of perceived visual aesthetics of web sites. International Journal of Human-Computer Studies 60(3), 269–298 (2004)CrossRefGoogle Scholar
  7. 7.
    Knight, J., Pandir, M.: Homepage aesthetics: The search for preference factors and the challenges of subjectivity. Interacting with Computers 18, 1351–1370 (2006)CrossRefGoogle Scholar
  8. 8.
    Ngo, D., Teo, L., Byrne, J.G.: Modelling interface aesthetics. Information Sciences 152, 25–46 (2003)CrossRefGoogle Scholar
  9. 9.
    Ngo, D., Byrne, J.: Application of an aesthetic evaluation model to data entry screens. Computers in Human Behavior 17(2), 149–185 (2001)CrossRefGoogle Scholar
  10. 10.
    Michailidou, E., Harper, S., Bechhofer, S.: Visual Complexity and Aesthetic Perception of Web Pages. In: SIGDOC 2008 Conference, Lisbon, pp. 215–224 (2008)Google Scholar
  11. 11.
    Purchase, H.C., et al.: Investigating objective measures of web page aesthetics and usability. In: Lutteroth, C., Shen, H. (eds.) Australasian User Interface Conference, pp. 19–28. CPRIT, Perth (2011)Google Scholar
  12. 12.
    Donderi, D., McFadden, S.: Compressed file length predicts search time and errors on visual displays. Displays 26, 71–78 (2005)CrossRefGoogle Scholar
  13. 13.
    Donderi, D.: An information theory analysis of visual complexity and dissimilarity. Perception 35, 823–835 (2006)CrossRefGoogle Scholar
  14. 14.
    Forsythe, A., et al.: Predicting beauty: Fractal dimension and visual complexity in art. British Journal of Psychology 102, 49–70 (2001)CrossRefGoogle Scholar
  15. 15.
    Oliva, A., et al.: Identifying the Perceptual Dimensions of Visual Complexity of Scenes. In: Cognitive Science Conference (2004)Google Scholar
  16. 16.
    Snodgrass, J.G., Vanderwart, M.: A Standardized Set of 260 Pictures. Norms for Name Agreement, Image Agreement, Familiarity and Visual Complexity. Journal of Experimental Psychology: Human Learning and Memory 6(2), 174–215 (1980)CrossRefGoogle Scholar
  17. 17.
    Mario, I., et al.: Image complexity measure: a human criterion free approach. In: North American Fuzzy Information Processing Society, pp. 241–246 (2005)Google Scholar
  18. 18.
    Salimun, C., Purchase, H.C., Simmons, D.: Visual aesthetics in computer interface design: does it matter? In: 34th European Conference on Visual Perception, p. 220 (2011)Google Scholar
  19. 19.
    International Commission on Illumination: Colour Difference, http://en.wikipedia.org/wiki/Color_difference#CIE76 (accessed February 28, 2012)
  20. 20.
    Robertson, A.: The CIE 1976 color-difference formulae. Colour Research and Application 2(1), 7–11 (1997)Google Scholar
  21. 21.
    Sharma, G.: Digital Color Imaging. IEEE Transactions on Image Processing 6(7), 901–932 (1997)CrossRefGoogle Scholar
  22. 22.
    Willow Garage: OpenCV, http://opencv.willowgarage.com/ (accessed February 28, 2012)
  23. 23.
    Ding, L., Goshtasby, A.: On the Canny edge detector. Pattern Recognition 34, 721–725 (2001)MATHCrossRefGoogle Scholar
  24. 24.
    Canny, J.: A Computational Approach to Edge Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 8(6), 679–698 (1986)CrossRefGoogle Scholar
  25. 25.
    ImageMagick Studio LLC: ImageMagick, http://www.imagemagick.org/ (accessed February 28, 2012)
  26. 26.
    Brace, N., Kemp, R., Snelgar, R.: SPSS for Psychologists, 2nd edn. Palgrave Macmillan (2003)Google Scholar
  27. 27.
    Ware, C.: Information Visualisation: Perception for Design. Elsevier (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Helen C. Purchase
    • 1
  • Euan Freeman
    • 1
  • John Hamer
    • 2
  1. 1.School of Computing ScienceUniversity of GlasgowGlasgowUnited Kingdom
  2. 2.Department of Computer ScienceUniversity of AucklandAucklandNew Zealand

Personalised recommendations