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Color compatibility from large datasets

Published: 25 July 2011 Publication History

Abstract

This paper studies color compatibility theories using large datasets, and develops new tools for choosing colors. There are three parts to this work. First, using on-line datasets, we test new and existing theories of human color preferences. For example, we test whether certain hues or hue templates may be preferred by viewers. Second, we learn quantitative models that score the quality of a five-color set of colors, called a color theme. Such models can be used to rate the quality of a new color theme. Third, we demonstrate simple proto-types that apply a learned model to tasks in color design, including improving existing themes and extracting themes from images.

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Published In

cover image ACM Transactions on Graphics
ACM Transactions on Graphics  Volume 30, Issue 4
July 2011
829 pages
ISSN:0730-0301
EISSN:1557-7368
DOI:10.1145/2010324
Issue’s Table of Contents
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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 July 2011
Published in TOG Volume 30, Issue 4

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  • (2024)Color Theme Evaluation through User Preference ModelingACM Transactions on Applied Perception10.1145/366532921:3(1-35)Online publication date: 29-Jul-2024
  • (2024)Palette, Purpose, Prototype: The Three Ps of Color Design and How Designers Navigate ThemProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3641976(1-19)Online publication date: 11-May-2024
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