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Measuring icon complexity: an automated analysis.

Alex Forsythe1, Noel Sheehy, Martin Sawey

  • 1School of Psychology, Queen's University, Belfast, Northern Ireland. a.forsythe@qub.ac.uk

Behavior Research Methods, Instruments, & Computers : a Journal of the Psychonomic Society, Inc
|July 2, 2003
PubMed
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Automated analysis of icon design properties can predict perceived complexity. Structural variability and edge information strongly correlate with human judgments, reducing the need for costly user surveys.

Area of Science:

  • Human-Computer Interaction
  • Cognitive Psychology
  • Visual Design

Background:

  • Assessing icon design complexity typically involves user surveys, which are time-consuming and expensive.
  • Developing an automated system for estimating perceived icon complexity could significantly reduce development costs and timelines.

Purpose of the Study:

  • To investigate the correlation between measurable icon properties and human judgments of perceived complexity.
  • To explore the potential of automated image-processing techniques for predicting icon complexity.

Main Methods:

  • Six icon properties were measured using image-processing software (Matlab): foreground elements, object count, hole count, edge information, and structural homogeneity.
  • These measured properties were correlated with established metrics of perceived icon complexity (Garcia et al., 1994; McDougall et al., 1999).

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Main Results:

  • Structural variability (r(s) = .65) and edge information (r(s) = .64) were the strongest correlates with human judgments of perceived icon complexity.
  • The findings suggest that objective, measurable properties of icon structure can reliably predict perceived complexity.

Conclusions:

  • Automated measurement of icon properties offers a viable and efficient alternative to traditional user surveys for assessing perceived complexity.
  • This approach can accelerate the icon design and development process by providing rapid, reliable feedback on complexity levels.