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CrowdColor: Crowdsourcing Color Perceptions Using Mobile Devices

Published: 24 August 2015 Publication History

Abstract

Providing accurate color information to online shopping customers is important for their purchase decisions. However, due to the multiple imaging processes that product photos undergo, end-users often experience a color mismatch between the color of the photo online and the product received. Therefore, we use a crowdsourcing approach to generate what we term CrowdColor, which is the collective color reported by individuals using a mobile color picker. CrowdColor serves as a color review application from the customers' perspectives in the form of a color palette that represents the product color. We perform controlled experiments to evaluate the accuracy of CrowdColor and to understand how the effects of the device and lighting conditions may influence the crowd's color perception and input tasks. The quantitative results reveal that CrowdColor achieves high accuracy and is positively rated overall. Based on experimental analyses, we present design guidelines for crowdsourcing color perception tasks.

Supplementary Material

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Supplemental movie and image files for, CrowdColor: Crowdsourcing Color Perceptions Using Mobile Devices

References

[1]
Farnsworth-Munsell 100 Hue Color Vision Test. http://www.color-blindness.com/fm100hue/
[2]
Hanrahan, P. and Lin, S. Modeling How People Extract Color Themes from Images. CHI '13, ACM (2013), 3101--3110.
[3]
Heer, J. and Bostock, M. Crowdsourcing Graphical Perception: Using Mechanical Turk to Assess Visualization Design. CHI '10, ACM (2010), 203--212.
[4]
Hill, B., Roger, T., and Vorhagen, F. W. Comparative analysis of the Quantization of Color Spaces on the Basis of the CIELAB Color-difference Formula. ACM Transactions on Graphics, ACM (1997), 16.2: 109--154.
[5]
Jain, J., et al. Color Match: an Imaging based Mobile Cosmetics Advisory Service. MobileHCI '08, ACM (2008), 331--334.
[6]
Kevin R. Parker, Philip S. Nitse, and Albert S. M. Tay. The Impact of Inaccurate Color on Customer Retention and CRM. Informing Science: the International Journal of an Emerging Transdiscipline, Volume 12, (2009), 105--121.
[7]
Kim, J., et al. Illuminant Adaptive Color Reproduction based on Lightness Adaptation and Flare for Mobile Phone. IEEE International Conference on Image Processing 2006. IEEE (2006), 1513--1516.
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Mantiuk, R., Daly, S., Kerofsky, L. Display adaptive tone mapping. ACM Transactions on Graphics. ACM (2008), 68.
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Stone, M. A Field Guide to Digital Color. Natick, MA. A. K. Peters, Ltd. (2003).

Cited By

View all
  • (2017)A crowdsourced approach to colormap assessmentProceedings of the Workshop on Reproducibility, Verification, and Validation in Visualization10.2312/eurorv3.20171106(1-5)Online publication date: 12-Jun-2017
  • (2017)TrailSenseProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/31318931:3(1-31)Online publication date: 11-Sep-2017
  • (2016)Enabling Designers to Foresee Which Colors Users Cannot SeeProceedings of the 2016 CHI Conference on Human Factors in Computing Systems10.1145/2858036.2858077(2693-2704)Online publication date: 7-May-2016
  • Show More Cited By

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  1. CrowdColor: Crowdsourcing Color Perceptions Using Mobile Devices

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    cover image ACM Conferences
    MobileHCI '15: Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services
    August 2015
    611 pages
    ISBN:9781450336529
    DOI:10.1145/2785830
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    New York, NY, United States

    Publication History

    Published: 24 August 2015

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    Author Tags

    1. Crowdsourcing
    2. color perception
    3. graphical perception
    4. mobile shopping

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    • MSIP/IITP

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    Overall Acceptance Rate 202 of 906 submissions, 22%

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    Cited By

    View all
    • (2017)A crowdsourced approach to colormap assessmentProceedings of the Workshop on Reproducibility, Verification, and Validation in Visualization10.2312/eurorv3.20171106(1-5)Online publication date: 12-Jun-2017
    • (2017)TrailSenseProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/31318931:3(1-31)Online publication date: 11-Sep-2017
    • (2016)Enabling Designers to Foresee Which Colors Users Cannot SeeProceedings of the 2016 CHI Conference on Human Factors in Computing Systems10.1145/2858036.2858077(2693-2704)Online publication date: 7-May-2016
    • (2016)Clothes radar: A crowdsourcing application collecting and displaying user clothes data2016 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia)10.1109/ICCE-Asia.2016.7804785(1-2)Online publication date: Oct-2016

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