Color Naming Models for Color Selection, Image Editing and Palette Design
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Color Naming Models for Color Selection, Image Editing and Palette Design Jeffrey Heer Maureen Stone Computer Science Department StoneSoup Consulting Stanford University Woodinville, WA [email protected] [email protected] ABSTRACT term. Berlin & Kay noted striking regularities among the Our ability to reliably name colors provides a link between use of color terms across cultures, leading them to posit 11 visual perception and symbolic cognition. In this paper, we universal basic color terms (in English: blue, brown, green, investigate how a statistical model of color naming can en- orange, pink, purple, red, yellow, black, grey, and white). able user interfaces to meaningfully mimic this link and sup- They observed that these terms were added to a language in port novel interactions. We present a method for construct- a similar progression across cultures. Subsequent decades ing a probabilistic model of color naming from a large, un- of research (e.g., [10, 14, 29, 39]) have challenged and ex- constrained set of human color name judgments. We de- tended this understanding. For example, more than 11 basic scribe how the model can be used to map between colors and color terms may exist (e.g., Russian contains two basic terms names and define metrics for color saliency (how reliably a for blue [39]), and language can have a relative effect on cat- color is named) and color name distance (the similarity be- egorical color perception and memory [29]. tween colors based on naming patterns). We then present a series of applications that demonstrate how color naming The importance of color names to perception and object clas- models can enhance graphical interfaces: a color dictionary sification has led researchers to formulate a variety models & thesaurus, name-based pixel selection methods for image describing how people associate names and colors [2,8, 20, editing, and evaluation aids for color palette design. 22, 24, 26]. Here, we extend prior models — in particular, the non-parametric approach of Chuang et al. [8] — to pro- Author Keywords pose a model-construction method for a collection of human Color names, color modeling, palette design, image editing, color-name judgments. We then demonstrate how the result- visualization, perception, XKCD ing model can be applied to enhance user interfaces. Our ACM Classification Keywords research contributions fall into two categories: H.5.2 Information Interfaces and Presentation: UI First, we contribute a method for constructing a proba- bilistic model of color naming from a large, unconstrained INTRODUCTION set of human color name judgments. We model color-name To reason and communicate about the world, people contin- associations using multinomial probability distributions that ually — and often effortlessly — categorize elements of their describe the likelihood of a color value given a color name, sensory experience. We perceive a world populated by ob- or vice versa. We then define useful operations in terms of jects that we label with named types, colors, shapes, tastes, this model. We apply our method to results from a large odors, and functions. Our capacity for categorization links web-based survey [25] containing over 3 million entries and sensing of the physical world to language and cognition, and over 100,000 unique color name responses. Our method in- underlies our ability to communicate and reference objects cludes a scaling technique that reduces this survey data to a in the world [16]. This observation suggests that user in- small set of maximally information-preserving color terms. terfaces that model human category judgments might enable We then describe how the model can be used to map between more compelling forms of reference and selection. colors and names and we define metrics for color saliency (how reliably a color is named) and color name distance (the In this paper, we explore this idea in the domain of color similarity between colors based on naming patterns). names — the linguistic labels used to describe colors. In- terest in color names as a means to investigate links between Second, we present novel interfaces enabled by our color perception, language, and cognition was spurred by the stud- naming model. These applications help users express color ies of Berlin & Kay [5] in the 1960s. They asked speakers of in new ways and offer designers new means for evaluating various languages to name a set of color stimuli and then se- their designs. We describe a color dictionary & thesaurus lect the most representative stimulus for each provided color tool for name-based color selection and browsing of color synonyms and antonyms. We then describe techniques for name-based pixel selection for image editing. We introduce methods to (1) suggest highly-probable color names to de- 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 scribe image pixels, (2) select image regions by color name, not made or distributed for profit or commercial advantage and that copies and (3) provide a new “magic wand” tool based on color bear this notice and the full citation on the first page. To copy otherwise, or name similarity. Finally, we show how color name overlap republish, to post on servers or to redistribute to lists, requires prior specific and saliency statistics supported by our model can aid the permission and/or a fee. design and evaluation of color palettes for visualization. CHI 2012, May 5–10, 2012, Austin, TX, USA. Copyright 2012 ACM 978-1-4503-0267-8/11/05...$5.00. RELATED WORK ages. However, this approach requires that the color vocab- Our research extends two streams of prior work: models of ulary be known a priori, and provides little insight into the color naming and user interfaces for color design. relative likelihood of color terms. Here, we seek to learn the kind and frequency of color terms used by people. Models of Color Naming In this paper, we build on the approach of Chuang et al. [8] to Researchers have devised multiple approaches for modeling construct a non-parametric probabilistic model of color nam- human color naming, both to aid scientific understanding ing. As we will describe, we modify their definition of color and to improve applications such as gamut mapping [24] and salience to improve comparability among colors and intro- image analysis [20, 22]. One approach is to simply parti- duce name-based color similarity measures. We demonstrate tion color space, e.g., by uniform subdivision of hue in HLS how to construct such models from a large, unconstrained set color space [17]. Another is to create a color dictionary that of color-name judgments. In particular, we use over 3 mil- maps color names (basic color terms plus modifiers such as lion color-name responses collected from readers of the web “light”, “dark”, and “vivid”) to a single color value. This comic XKCD. While prior work [22] has required that the approach is used by the ISCC-NBS standard [15] and de- desired number of color names be given as a model param- rived variants [4, 11, 20]. These approaches map colors to eter, we introduce a scaling routine that uses a measure of names in a deterministic, disjoint fashion: they do not model information loss to determine the number of color names. association strength or overlap among color names. Tools for Color Design Instead, color scientists use statistical models fitted to a cor- Researchers in HCI, Computer Graphics, and Visualization pus of human color-name judgments. Some researchers em- have devised myriad tools and algorithms for assisting col- ploy parametric models using a mixture of Gaussian [24, orists. One prominent class of applications is interfaces for 26] or Gaussian-Sigmoid distributions [2]. While paramet- color selection or palette design [1,3, 12, 19, 28, 37]. For ric models can suppress noise and be described with a small example, PRAVDAColor [3] and ColorBrewer [12] suggest set of parameters, they make assumptions about the shape of color palettes for encoding data in visualizations based on named color regions that may not match empirical distribu- data types and/or cardinality. Meier et al. [19] apply theories tions. For example, a Gaussian ellipsoid may assign proba- of artistic color harmony [13, 18] to assist interactive color bility mass to color values outside the gamut of interest. selection. Cohen-Or et al. [9] use these same theories to for- mulate automated color harmonization methods for image In response to these shortcomings, others have advocated composition. To our knowledge, none of these tools incor- the use of non-parametric models that avoid assumptions re- porate nuanced models of color naming to aid color selection garding the shape of color name distributions. Moroney [22] or image analysis. In this paper, we explore ways in which models color naming using histograms over a binned color color naming models can augment color picking, pixel selec- space. Chuang et al. [8] model color-name association using tion for image editing, and the evaluation of color palettes. multinomial conditional probability distributions. Chuang et al. also define a statistic for color salience — the unique- ness of a color name — in terms of model entropy. Using the CONSTRUCTING COLOR NAMING MODELS 330 colors of the World Color Survey [10], they find good To construct a color naming model, we first process the in- agreement among high salience scores and basic color name put data: a collection of color-name pairs comprising nam- “foci” identified in previous work [5, 35]. ing judgments by human subjects. We present a method for reducing unconstrained text responses describing millions of One issue affecting model construction is the size and gran- unique colors into a compact table of color-name correspon- ularity of training data.