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    Icon Set Selection via Human Computation

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    Date
    2016
    Author
    Laursen, Lasse Farnung
    Koyama, Yuki
    Chen, Hsiang-Ting
    Garces, Elena
    Gutierrez, Diego
    Harper, Richard
    Igarashi, Takeo
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    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.
    BibTeX
    @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}
    }
    URI
    http://dx.doi.org/10.2312/pg.20161326
    https://diglib.eg.org:443/handle/10.2312/pg20161326
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    Eurographics Association copyright © 2013 - 2023 
    Send Feedback | Contact - Imprint | Data Privacy Policy | Disable Google Analytics
    Theme by @mire NV
    System hosted at  Graz University of Technology.
    TUGFhA