Affective Color in Visualization

Bartram, Lyn; Patra, Abhisekh; Stone, Maureen

doi:10.1145/3025453.3026041

Cited by 104 publications

(95 citation statements)

References 29 publications

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“…First and foremost, colors that represent different categories must appear different; perceptually discriminable [8,15,45,46]. Other considerations include selecting colors that have distinct names [16], are aesthetically preferable [12], or evoke particular emotions [4]. Most relevant to the present work, it is desirable to select semantically interpretable color palettes to help people interpret the meanings of colors in visualizations [23,41,43].…”

Section: Related Workmentioning

confidence: 99%

“…An alternative approach is to estimate human color-concept associations from large databases, such as tagged images [4,[23][24][25]43], color naming data sets [14,24,43], semantic networks [14], and natural language corpora [43]. Each type of database enables linking colors with concepts but has strengths and weaknesses, so automated methods often combine information from multiple databases [14,24,25,43].…”

Section: Quantifying Color-concept Associationsmentioning

confidence: 99%

“…Recent research has investigated how to optimize color palette design to produce color-concept assignments that are easy to interpret [23,41,43]. Methods typically involve quantifying associations between each color and concept of interest, and then using those data to calculate optimal color-concept assignments for the visualization [4,23,41,43]. It may seem that the best approach would be to assign concepts to their most strongly associated colors, but that is not always the case.…”

Section: Introductionmentioning

confidence: 99%

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Estimating Color-Concept Associations from Image Statistics

Rathore

Leggon

Lessard

et al. 2019

IEEE Trans. Visual. Comput. Graphics

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50 0 50 100 b* R O Y G Br Gr Pu Pi Pi B r = 1 Balls Sectors Categories Δr = 1 Δr = 1 Δh = 5°Δ r = 1 Δh = 40°Δ r = 20 Δh = 5°Δ r = 20 50 0 -50 -50 Δr = 20 Δh = 40°a * Figure 1. We constructed models that estimate human color-concept associations using color distributions extracted from images of relevant concepts. We compared methods for extracting color distributions by defining different kinds of color tolerance regions (white outlines) around each target color (regularly spaced large dots) in CIELAB space. Subplots show a planar view of CIELAB space at L* = 50, with color tolerance regions defined as balls (left column; radius ∆r ), cylindrical sectors (middle columns; radius ∆r and hue angle ∆h), and category boundaries around each target color (right column; Red, Orange, Yellow, Green, Blue, Purple, Pink, Brown, Gray; white and black not shown). Each target color is counted as "present" in the image each time any color in its tolerance region is observed. This has a smoothing effect, which enables the inclusion of colors that are not present in the image but similar to colors that are. A model that includes two sector features and a category feature best approximated human color-concept associations for unseen concepts and images (see text for details).Abstract-To interpret the meanings of colors in visualizations of categorical information, people must determine how distinct colors correspond to different concepts. This process is easier when assignments between colors and concepts in visualizations match people's expectations, making color palettes semantically interpretable. Efforts have been underway to optimize color palette design for semantic interpretablity, but this requires having good estimates of human color-concept associations. Obtaining these data from humans is costly, which motivates the need for automated methods. We developed and evaluated a new method for automatically estimating color-concept associations in a way that strongly correlates with human ratings. Building on prior studies using Google Images, our approach operates directly on Google Image search results without the need for humans in the loop. Specifically, we evaluated several methods for extracting raw pixel content of the images in order to best estimate color-concept associations obtained from human ratings. The most effective method extracted colors using a combination of cylindrical sectors and color categories in color space. We demonstrate that our approach can accurately estimate average human color-concept associations for different fruits using only a small set of images. The approach also generalizes moderately well to more complicated recycling-related concepts of objects that can appear in any color.

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Section: Related Workmentioning

confidence: 99%

Section: Quantifying Color-concept Associationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Estimating Color-Concept Associations from Image Statistics

Rathore

Leggon

Lessard

et al. 2019

IEEE Trans. Visual. Comput. Graphics

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“…Visualizations commonly use color ramps to encode ordered or continuous numeric data. The specific colors used in a given visualization determine how accurately that visualization communicates the underlying data and influence subjective impressions of a visualization, such as affect [6], topical alignment [30,41], and aesthetic quality [37]. Heuristics for constructing effective encodings have evolved from years of experience by designers.…”

Section: Introductionmentioning

confidence: 99%

Color Crafting: Automating the Construction of Designer Quality Color Ramps

Smart

Szafir

2020

IEEE Trans. Visual. Comput. Graphics

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Fig. 1. Our approach uses design mining and unsupervised clustering techniques to produce automatically generated color ramps that capture designer practices. The four choropleth maps shown above utilize color ramps generated from our approach. Developers select a single guiding seed color, shown in the squares below each map, to generate a ramp. We then fit curves capturing structural patterns in designer practices in CIELAB (bottom) to these seed colors to generate ramps (middle, seed colors indicated by a black dot). We embody this technique in Color Crafter, a web-based tool that enables designers of all ability levels to generate high-quality custom color ramps.Abstract-Visualizations often encode numeric data using sequential and diverging color ramps. Effective ramps use colors that are sufficiently discriminable, align well with the data, and are aesthetically pleasing. Designers rely on years of experience to create high-quality color ramps. However, it is challenging for novice visualization developers that lack this experience to craft effective ramps as most guidelines for constructing ramps are loosely defined qualitative heuristics that are often difficult to apply. Our goal is to enable visualization developers to readily create effective color encodings using a single seed color. We do this using an algorithmic approach that models designer practices by analyzing patterns in the structure of designer-crafted color ramps. We construct these models from a corpus of 222 expert-designed color ramps, and use the results to automatically generate ramps that mimic designer practices. We evaluate our approach through an empirical study comparing the outputs of our approach with designer-crafted color ramps. Our models produce ramps that support accurate and aesthetically pleasing visualizations at least as well as designer ramps and that outperform conventional mathematical approaches.

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“…Different colour palettes can communicate different colour meanings or induce different colour emotions. 15 Often colour palettes are generated by designers for practical application based on their knowledge of aesthetics with respect to either a design brief or the designer's own colour preferences. 16,17 Colour palettes may be extracted automatically from an image or a set of images 18 or even generated from a word.…”

Section: Introductionmentioning

confidence: 99%

Predicting visual similarity between colour palettes

Yang

Chen

Xiao

2020

Color Research & Application

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This work is concerned with the prediction of visual colour difference between pairs of palettes. In this study, the palettes contained five colours arranged in a horizontal row. A total of 95 pairs of palettes were rated for visual difference by 20 participants. The colour difference between the palettes was predicted using two algorithms, each based on one of six colour‐difference formulae. The best performance (r2 = 0.86 and STRESS = 16.9) was obtained using the minimum colour‐difference algorithm (MICDM) using the CIEDE2000 equation with a lightness weighing of 2. There was some evidence that the order (or arrangement) of the colours in the palettes was a factor affecting the visual colour differences although the MICDM algorithm does not take order into account. Application of this algorithm is intended for digital design workflows where colour palettes are generated automatically using machine learning and for comparing palettes obtained from psychophysical studies to explore, for example, the effect of culture, age, or gender on colour associations.

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Affective Color in Visualization

Cited by 104 publications

References 29 publications

Estimating Color-Concept Associations from Image Statistics

Estimating Color-Concept Associations from Image Statistics

Color Crafting: Automating the Construction of Designer Quality Color Ramps

Predicting visual similarity between colour palettes

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