title: Measuring icon complexity: an automated analysis.
abstract: Measures of icon designs rely heavily on surveys of the perceptions of population samples. Thus, measuring the extent to which changes in the structure of an icon will alter its perceived complexity can be costly and slow. An automated system capable of producing reliable estimates of perceived complexity could reduce development costs and time. Measures of icon complexity developed by Garcia, Badre, and Stasko (1994) and McDougall, Curry, and de Bruijn (1999) were correlated with six icon properties measured using Matlab (MathWorks, 2001) software, which uses image-processing techniques to measure icon properties. The six icon properties measured were icon foreground, the number of objects in an icon, the number of holes in those objects, and two calculations of icon edges and homogeneity in icon structure. The strongest correlates with human judgments of perceived icon complexity (McDougall et al., 1999) were structural variability (r(s) = .65) and edge information (r(s) = .64).
authors.affiliation: School of Psychology, Queen's University, Belfast, Northern Ireland. a.forsythe@qub.ac.uk
authors.forename: Alex
authors.initials: A
authors.lastname: Forsythe
authors.forename: Noel
authors.initials: N
authors.lastname: Sheehy
authors.forename: Martin
authors.initials: M
authors.lastname: Sawey
mesh_terms: D003142:Communication; D001330:Electronic Data Processing / Q000379:methods; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D007802:Language; D008432:Mathematical Computing; D010364:Pattern Recognition, Visual; D011578:Psycholinguistics / Q000295:instrumentation; D012984:Software; D013561:Symbolism; D014584:User-Computer Interface; D014796:Visual Perception