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Feature congestion: a measure of display clutter

Published:02 April 2005Publication History

ABSTRACT

Management of clutter is an important factor in the design of user interfaces and information visualizations, allowing improved usability and aesthetics. However, clutter is not a well defined concept. In this paper, we present the Feature Congestion measure of display clutter. This measure is based upon extensive modeling of the saliency of elements of a display, and upon a new operational definition of clutter. The current implementation is based upon two features: color and luminance contrast. We have tested this measure on maps that observers ranked by perceived clutter. Results show good agreement between the observers' rankings and our measure of clutter. Furthermore, our measure can be used to make design suggestions in an automated UI critiquing tool.

References

  1. Ahlberg, C. & Shneiderman, B. Visual Information Seeking: Tight Coupling of Dynamic Query Filters with Starfield Displays. Proc. CHI 1994, ACM Press (1994), 313--317.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Beyond the Click: Insights from Marketing Effectiveness Research (January 2001). http://www.dynamic]]Google ScholarGoogle Scholar
  3. Burt, P., & Adelson, E.H. The Laplacian Pyramid as a Compact Image Code. IEEE Trans. on Communication, COM-31:532--540, 1983.]]Google ScholarGoogle ScholarCross RefCross Ref
  4. Buttenfield, B.P. & McMaster, R.B. (eds.). Map Generalization: Making Rules for Knowledge Representation. Longman, London, 1991.]]Google ScholarGoogle Scholar
  5. Callaghan, T.C. Interference and domination in texture segregation: Hue, geometric form, and line orientation. Perception & Psychophysics 46, 4 (1989), 299--311.]]Google ScholarGoogle ScholarCross RefCross Ref
  6. C.I.E. Recommendations on uniform color spaces, color difference equations, psychometric color terms. Supplement No.2 to CIE publication No.15 (E.-1.3.1) 1971/(TC-1.3.) (1978).]]Google ScholarGoogle Scholar
  7. Duncan, J. & Humphreys, G.W. Visual search and stimulus similarity. Psychol. Rev., 96, (1989), 433--458.]]Google ScholarGoogle ScholarCross RefCross Ref
  8. Eckstein, M.P., Thomas, J.P., Palmer, J, & Shimozaki, S.S. A signal detection model predicts the effects of set-size in visual search accuracy for feature, conjunction and disjunction displays, Perception and Psychophysics,62, 3 (2000), 425-451.]]Google ScholarGoogle ScholarCross RefCross Ref
  9. Fishkin, K. & Stone, M.C. Enhanced Dynamic Queries via Movable Filters. Proc. CHI 1995, ACM Press (1995), 415--420.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Frank, A.U. & Timpf, S. Multiple Representations for Cartographic Objects in a Multi-scale Tree - An Intelligent Graphical Zoom. Comput. & Graphics, 18, 6 (1994), 823--829.]]Google ScholarGoogle ScholarCross RefCross Ref
  11. Furnas, G.W. Generalized Fisheye Views. Proc. CHI 1986, ACM Press (1986), 16--23.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Itti, L., Koch, C., & Niebur, E. A Model of Saliency-Based Visual Attention for Rapid Scene Analysis. IEEE Trans. on Pattern Analysis and Machine Intelligence, 20, 11, (1998), 1254--1259.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Miller, G.A. The Magical Number Seven, Plus or Minus Two (http://psychclassics.yorku.ca/Miller/). Psychological Review, 63, (1956), pp. 81--97.]]Google ScholarGoogle ScholarCross RefCross Ref
  14. Nickerson, J.V. Visual Programming. Ph.D. dissertation, New York University, New York, 1994. http://www.stevens-tech.edu/jnickerson/.]]Google ScholarGoogle Scholar
  15. Nygren, E. & Allard, A. "Between the Clicks": Skilled Users Scanning of Pages. In Designing for the Web: Empirical Studies, Human Factors and the Web.. Sandia National Laboratories, Albuquerque, NM (March 1996).]]Google ScholarGoogle Scholar
  16. Oliva, A., Mack, M.L., Shrestha, M., & Peeper, A. Identifying the Perceptual Dimensions of Visual Complexity of Scenes. Proc. 26th Annual Meeting of the Cognitive Science Society, (2004).]]Google ScholarGoogle Scholar
  17. Palmer, J. Set-size Effects in Visual Search: the Effect of Attention is Independent of the Stimulus for Simple Tasks. Vision Research, 34, (1994), 1703--1721.]]Google ScholarGoogle ScholarCross RefCross Ref
  18. Perlin, K. & Fox, D. Pad: An Alternative Approach to the Computer Interface. Proc. SIGGRAPH 1993, ACM Press (1993), 57--64.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Perona, P. & Malik, J. Preattentive texture discrimination with early vision mechanisms. JOSA(A) 7, 5, (1990), 923--932.]]Google ScholarGoogle Scholar
  20. Phillips, R.J. & Noyes, L. An Investigation of Visual Clutter in the Topographic Base of a Geological Map. The Cartographic Journal, 19, 2 (1982), 122--132.]]Google ScholarGoogle ScholarCross RefCross Ref
  21. Rosenholtz, R. Significantly different textures: A computational model of pre-attentive texture segmentation. Proc. European Conference on Computer Vision, Springer Verlag (2000), 197--211.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Rosenholtz, R. Search asymmetries? What search asymmetries? Perception & Psychophysics, 63, 3, (2001), 476--489.]]Google ScholarGoogle ScholarCross RefCross Ref
  23. Rosenholtz, R. Visual search for orientation among heterogeneous distractors: Experimental results and implications for signal detection theory models of search. J. Experimental Psychology, 27, 4, (2001), 985--999.]]Google ScholarGoogle Scholar
  24. Sellen, A.J. & Harper, R.H.R. The Myth of the Paperless Office. MIT Press, Cambridge, MA, 2003.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Springer, C.J. Retrieval of Information from Complex Alphanumeric Displays: Screen Formatting Variables' Effects on Target Identification Time. Proc. 2nd Int'l Conf. on Human-Computer Interaction, Elsevier Science Pub. (1987), 375--382.]]Google ScholarGoogle Scholar
  26. Treisman, A.M., & Gelade, G. A feature-integration theory of attention. Cog. Psych., 12, (1980), 97--136.]]Google ScholarGoogle ScholarCross RefCross Ref
  27. Tullis, T.S. A Computer-Based Tool for Evaluating Alphanumeric Displays. INTERACT '84, B. Shackel (ed.), Elsevier Science (1985), 719--723.]]Google ScholarGoogle Scholar
  28. Tufte, E.R. The Visual Display of Quantitative Information. Graphics Press, Cheshire, CT, 1983.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Watson, A.B. Visual detection of spatial contrast patterns: Evaluation of five simple models. Opt. Express, 6, (2000), 12-33, http://www.opticsexpress.org/abstract.]]Google ScholarGoogle ScholarCross RefCross Ref
  30. Wolfe, J.M. Guided Search 2.0: A Revised Model of Visual Search. Psychonomic Bulletin & Review, 1, 2, (1994), 202--238.]]Google ScholarGoogle Scholar
  31. Wolfe, J.M. Visual search. In H. Pashler, ed., Attention. University College London Press, London, U.K., 1998.]]Google ScholarGoogle Scholar

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      cover image ACM Conferences
      CHI '05: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
      April 2005
      928 pages
      ISBN:1581139985
      DOI:10.1145/1054972

      Copyright © 2005 ACM

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      Publication History

      • Published: 2 April 2005

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