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Icon set selection via human computation

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

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Icon set selection via human computation. / Laursen, Lasse Farnung; Koyama, Yuki; Chen, Hsiang-Ting et al.
Proceedings of the 24th Pacific Conference on Computer Graphics and Applications: Short Papers. Goslar Germany, Germany: Eurographics Association , 2016. (PG '16).

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Laursen, LF, Koyama, Y, Chen, H-T, Garces, E, Gutierrez, D, Harper, R & Igarashi, T 2016, Icon set selection via human computation. in Proceedings of the 24th Pacific Conference on Computer Graphics and Applications: Short Papers. PG '16, Eurographics Association , Goslar Germany, Germany. https://doi.org/10.2312/pg.20161326

APA

Laursen, L. F., Koyama, Y., Chen, H-T., Garces, E., Gutierrez, D., Harper, R., & Igarashi, T. (2016). Icon set selection via human computation. In Proceedings of the 24th Pacific Conference on Computer Graphics and Applications: Short Papers (PG '16). Eurographics Association . https://doi.org/10.2312/pg.20161326

Vancouver

Laursen LF, Koyama Y, Chen H-T, Garces E, Gutierrez D, Harper R et al. Icon set selection via human computation. In Proceedings of the 24th Pacific Conference on Computer Graphics and Applications: Short Papers. Goslar Germany, Germany: Eurographics Association . 2016. (PG '16). doi: 10.2312/pg.20161326

Author

Laursen, Lasse Farnung ; Koyama, Yuki ; Chen, Hsiang-Ting et al. / Icon set selection via human computation. Proceedings of the 24th Pacific Conference on Computer Graphics and Applications: Short Papers. Goslar Germany, Germany : Eurographics Association , 2016. (PG '16).

Bibtex

@inproceedings{12f1427aed2341f9b6d0242c1c306ebb,
title = "Icon set selection via human computation",
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.",
author = "Laursen, {Lasse Farnung} and Yuki Koyama and Hsiang-Ting Chen and Elena Garces and Diego Gutierrez and Richard Harper and Takeo Igarashi",
year = "2016",
month = aug,
day = "1",
doi = "10.2312/pg.20161326",
language = "English",
isbn = "9783038680246",
series = "PG '16",
publisher = "Eurographics Association ",
booktitle = "Proceedings of the 24th Pacific Conference on Computer Graphics and Applications",

}

RIS

TY - GEN

T1 - Icon set selection via human computation

AU - Laursen, Lasse Farnung

AU - Koyama, Yuki

AU - Chen, Hsiang-Ting

AU - Garces, Elena

AU - Gutierrez, Diego

AU - Harper, Richard

AU - Igarashi, Takeo

PY - 2016/8/1

Y1 - 2016/8/1

N2 - 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.

AB - 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.

U2 - 10.2312/pg.20161326

DO - 10.2312/pg.20161326

M3 - Conference contribution/Paper

SN - 9783038680246

T3 - PG '16

BT - Proceedings of the 24th Pacific Conference on Computer Graphics and Applications

PB - Eurographics Association

CY - Goslar Germany, Germany

ER -