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Icon set selection via human computation

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  • Lasse Farnung Laursen
  • Yuki Koyama
  • Hsiang-Ting Chen
  • Elena Garces
  • Diego Gutierrez
  • Richard Harper
  • Takeo Igarashi
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Publication date1/08/2016
Host publicationProceedings of the 24th Pacific Conference on Computer Graphics and Applications: Short Papers
Place of PublicationGoslar Germany, Germany
PublisherEurographics Association
Number of pages6
ISBN (print)9783038680246
<mark>Original language</mark>English

Publication series

NamePG '16
PublisherEurographics Association

Abstract

Picking the best icons for a graphical user interface is difficult. We present a new method which, given several icon candidates representing functionality, selects a complete icon set optimized for comprehensibility and identifiability. These two properties are measured using human computation. We apply our method to a domain with a less established iconography and produce several icon sets. To evaluate our method, we conduct a user study comparing these icon sets and a designer-picked set. Our estimated comprehensibility score correlate with the percentage of correctly understood icons, and our method produces an icon set with a higher comprehensibility score than the set picked by an involved icon designer. The estimated identifiability score and related tests did not yield significant findings. Our method is easy to integrate in traditional icon design workflow and is intended for use by both icon designers, and clients of icon designers.