Icon Set Selection via Human Computation
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Date
2016
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics 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.
Description
@inproceedings{10.2312:pg.20161326,
booktitle = {Pacific Graphics Short Papers},
editor = {Eitan Grinspun and Bernd Bickel and Yoshinori Dobashi},
title = {{Icon Set Selection via Human Computation}},
author = {Laursen, Lasse Farnung and Koyama, Yuki and Chen, Hsiang-Ting and Garces, Elena and Gutierrez, Diego and Harper, Richard and Igarashi, Takeo},
year = {2016},
publisher = {The Eurographics Association},
ISSN = {-},
ISBN = {978-3-03868-024-6},
DOI = {10.2312/pg.20161326}
}