Art, Affect and Color: Creating Engaging Expressive Scientific Visualization

Samsel, Francesca; Bartram, Lyn; Bares, Annie

doi:10.1109/visap45312.2018.9046053

Cited by 11 publications

(3 citation statements)

References 37 publications

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“…One of them is color combination selection. Constructing an optimal color combination is a critical factor for data visualization [13,14], and a lot of literature paid their attention to it [15][16][17][18][19]. A method to improve the readability of the data after clustering and visualizing is to consider the degree of color difference, which can be measured by the distance of two colors [20].…”

Section: Color Combination Selectionmentioning

confidence: 99%

The Extraction of Maximal-Sum Principal Submatrix and Its Applications

Zhang

Luo

2023

Algorithms

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Extracting k-order maximal-sum principal submatrix from an n-order real matrix is a typical combinatorial optimization problem and an NP-hard problem. To improve the computational efficiency of solving this problem, we, in this paper, propose an accelerated algorithm with row-by-row updates, called the fusion row update accelerated algorithm, which works by reducing the number of addition operations for submatrix elements. The new algorithm is applied to accelerate color combination selection and maximize color difference, which improves the readability of data visualization results; it is also applied to accelerate stock investment portfolio selection and minimize correlation degree, which decreases the investment risk in the view of daily return volatility.

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Section: Color Combination Selectionmentioning

confidence: 99%

The Extraction of Maximal-Sum Principal Submatrix and Its Applications

Zhang

Luo

2023

Algorithms

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“…For example, Jahanian et al leverage design mining to model color semantics from magazine cover designs [30] and visual balance from aesthetically pleasing photographs [31]. Samsel et al [63] use color schemes from well-known paintings to generate more engaging and expressive scientific visualizations. Our approach builds on these works, utilizing the principles of design mining to infer aspects of color ramp design directly from high-quality examples.…”

Section: Automated Design and Design Mining In Visualizationmentioning

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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“…Our Color Loom applet extracts the color palette automatically from source images, which can be works of art, similar to manual approach introduced by Vote et al [75]. Because they are quick to create, and artists can tune the results based on the data, the resulting colormaps are often useful for revealing more information in specific datasets [54,66] or even better engaging users, as in recent studies of affective use of color [64].…”

Section: Colormaps and Textures For Visualizationmentioning

confidence: 99%

Artifact-Based Rendering: Harnessing Natural and Traditional Visual Media for More Expressive and Engaging 3D Visualizations

Johnson

Samsel

Abram

et al. 2019

IEEE Trans. Visual. Comput. Graphics

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Fig. 1. Using traditional physical artistic media as input to the digital visualization pipeline provides a richer visual vocabulary and opens the door for artists to participate in creating more expressive and engaging 3D scientific visualizations. This example helps scientists understand commercially viable macroalgae growth in the Gulf of Mexico by encoding temperature and salinity from remote sensing together with eddy direction and curvature and three nitrate concentrations from computational simulation.Abstract-We introduce Artifact-Based Rendering (ABR), a framework of tools, algorithms, and processes that makes it possible to produce real, data-driven 3D scientific visualizations with a visual language derived entirely from colors, lines, textures, and forms created using traditional physical media or found in nature. A theory and process for ABR is presented to address three current needs: (i) designing better visualizations by making it possible for non-programmers to rapidly design and critique many alternative data-to-visual mappings; (ii) expanding the visual vocabulary used in scientific visualizations to depict increasingly complex multivariate data; (iii) bringing a more engaging, natural, and human-relatable handcrafted aesthetic to data visualization. New tools and algorithms to support ABR include front-end applets for constructing artifact-based colormaps, optimizing 3D scanned meshes for use in data visualization, and synthesizing textures from artifacts. These are complemented by an interactive rendering engine with custom algorithms and interfaces that demonstrate multiple new visual styles for depicting point, line, surface, and volume data. A within-the-research-team design study provides early evidence of the shift in visualization design processes that ABR is believed to enable when compared to traditional scientific visualization systems. Qualitative user feedback on applications to climate science and brain imaging support the utility of ABR for scientific discovery and public communication.

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Art, Affect and Color: Creating Engaging Expressive Scientific Visualization

Cited by 11 publications

References 37 publications

The Extraction of Maximal-Sum Principal Submatrix and Its Applications

The Extraction of Maximal-Sum Principal Submatrix and Its Applications

Color Crafting: Automating the Construction of Designer Quality Color Ramps

Artifact-Based Rendering: Harnessing Natural and Traditional Visual Media for More Expressive and Engaging 3D Visualizations

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