Icon set selection via human computation

Laursen, LF; Koyama, Y; Chen, HT; Garces, E; Gutierrez, D; Harper, R; Igarashi, T

Icon set selection via human computation

Laursen, LF Koyama, Y Chen, HT Garces, E Gutierrez, D Harper, R Igarashi, T

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Publication Type:: Conference Proceeding
Citation:: Proceedings - Pacific Conference on Computer Graphics and Applications, 2016, 2016-October, pp. 1-6
Issue Date:: 2016-01-01

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Field	Value	Language
dc.contributor.author	Laursen, LF
dc.contributor.author	Koyama, Y
dc.contributor.author	Chen, HT
dc.contributor.author	Garces, E
dc.contributor.author	Gutierrez, D
dc.contributor.author	Harper, R
dc.contributor.author	Igarashi, T
dc.date.accessioned	2023-02-06T00:00:03Z
dc.date.available	2023-02-06T00:00:03Z
dc.date.issued	2016-01-01
dc.identifier.citation	Proceedings - Pacific Conference on Computer Graphics and Applications, 2016, 2016-October, pp. 1-6
dc.identifier.isbn	9783038680246
dc.identifier.issn	1550-4085
dc.identifier.uri	http://hdl.handle.net/10453/165911
dc.description.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.
dc.language	en
dc.relation.ispartof	Proceedings - Pacific Conference on Computer Graphics and Applications
dc.relation.isbasedon	10.2312/pg.20161326
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Icon set selection via human computation
dc.type	Conference Proceeding
utslib.citation.volume	2016-October
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2023-02-06T00:00:00Z
pubs.publication-status	Published
pubs.volume	2016-October

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.

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http://hdl.handle.net/10453/165911