Analysis of Web Page Complexity Through Visual Segmentation

  • Guangfeng Song
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4553)


Web pages have increasingly been used as the user interface of many software systems. The simplicity of interaction with web pages is a desirable advantage of using them. However, the user interface can also get more complex when more complex web pages are used to build it. Understanding the complexity of web pages as perceived subjectively by users is therefore important to better design this type of user interface. This paper reports an analysis of web page complexity through visual segmentation of web pages. 100 web pages were visually segmented by human participants as well as a computer program using Gestalt principles. The participants also indicated the perceived complexity of the web pages during the experiment. The result shows the perception of complexity is highly subjective but may be reliably measured. The number of blocks resulted from the three segmentation methods seemed to be irrelevant to perceived complexity. However, a composite metric that incorporate visual block information and other data of web pages seems to be promising in predicting the perceived complexity.


Visual Segmentation Interface Complexity 


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  1. 1.
    Campbell, D.J.: Task Complexity: A Review and Analysis. Academy of Management Review 13, 40–52 (1988)CrossRefGoogle Scholar
  2. 2.
    Grassberger, P.: Information and Complexity Measures in Dynamical Systems. In: Atmanspacher, H., Scheingraber, H. (eds.) Information Dynamics, pp. 15–33. Plenum Press, New York (1991)Google Scholar
  3. 3.
    Piasecki, R., Martin, M.T., Plastino, A.: Inhomogeneity and Complexity Measures for Spatial Patterns. Physica A 307, 157–171 (2002)zbMATHCrossRefGoogle Scholar
  4. 4.
    Patel, L.N., Holt, P.: Testing a Computational Model of Visual Complexity in Background Images. In: The Proceedings of ACIVS, Baden, pp. 119–123 (2000)Google Scholar
  5. 5.
    Edmonds, B.: What Is Complexity? –The PhiloBaldonado, M., Chang, C.-C.K., Gravano, L., Paepcke, A.: The Stanford Digital Library Metadata Architecture. Int. J. Digit. Libr. 1, 108–121 (1997)CrossRefGoogle Scholar
  6. 6.
    Drozdz, S., Kwapien, J., Speth, J., Wojcik, M.: Identifying Complexity by Means of Matrices. Physica A 314, 355–361 (2002)zbMATHCrossRefGoogle Scholar
  7. 7.
    Nielsen, J.: F-Shaped Pattern For Reading Web Content (2006) (retrieved January 20, 2007), from
  8. 8.
    Hurst, M.: Layout and language: Challenges for table understanding on the web. In: Proceedings of the 1st International Workshop on Web Document Analysis. Seattle WA, pp. 27–30 (2001)Google Scholar
  9. 9.
    Song, R., Liu, H., Wen, J., Ma, W.: Learning Block Importance Models For Web Pages. In: Proceedings of the 13th international Conference on World Wide Web. New York, NY, pp. 203–211 (2004)Google Scholar
  10. 10.
    Marcotegui, B.: Segmentation Algorithm by Multicriteria Region Merging. In: Maragos, P., Schafer, R.W., Butt, M.A. (eds.) Mathematical Morphology and its Applications to Image and Signal Processing, pp. 313–320. Kluwer Academic Pub., Boston (1996)Google Scholar
  11. 11.
    Tullis, T.S.: A Computer-based Tool For Evaluating Alphanumeric Displays. In: Shackel, B. (ed.): Human-Computer Interaction: INTERACT 1984. London, England, pp. 719–723 (1984)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Guangfeng Song
    • 1
  1. 1.Dept. of information science and Technology, Penn State Greater Allegheny, 4000 University Dr, McKeesport, PA 

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