Visual Analytics and Imaging Laboratory (VAI Lab)
Computer Science Department, Stony Brook University, NY

ColorMapND: A Data-Driven Approach and Tool for Mapping Multivariate Data to Color

Abstract: A wide variety of color schemes have been devised for mapping scalar data to color. We address the challenge of color-mapping multivariate data. While a number of methods can map low-dimensional data to color, for example, using bilinear or barycentric interpolation for two or three variables, these methods do not scale to higher data dimensions. Likewise, schemes that take a more artistic approach through color mixing and the like also face limits when it comes to the number of variables they can encode. Our approach does not have these limitations. It is data driven in that it determines a proper and consistent color map from first embedding the data samples into a circular interactive multivariate color mapping display (ICD) and then fusing this display with a convex (CIE HCL) color space. The variables (data attributes) are arranged in terms of their similarity and mapped to the ICD’s boundary to control the embedding. Using this layout, the color of a multivariate data sample is then obtained via modified generalized barycentric coordinate interpolation of the map. The system we devised has facilities for contrast and feature enhancement, supports both regular and irregular grids, can deal with multi-field as well as multispectral data, and can produce heat maps, choropleth maps, and diagrams such as scatterplots.

Teaser: This is the system interface with all major displays and components, using a battery dataset with four chemical components:

Users can select a multivariate data point in any of the interface displays via mouse click. The system responds by highlighting the selected data point with a small circle both in the targeted display as well as in the other, synched displays (see arrows, added for illustration). (a) Integrated CIE HCL (Hue Chroma Luminance) interactive multivariate color mapping display (ICD, top) with control panel (middle), and the selected point’s multivariate spectrum display (bottom). (b) Multi-field / hyperspectral image, pseudo-colored via the multivariate color map in (a). (c) Locally enhanced colorization of the selected rectangular region in (b). (d) Individual scalar images (usually displayed on the bottom of the interface in a channel view partition) colorized via the attribute-linked color primaries marked and labeled at the circle boundary of the multivariate color map in (a). The image in (b) constitutes a joint colorization of these individual channel images.

Video: Watch it to get a quick overview:

Paper: S. Cheng. W. Xu, K. Mueller, “ColorMapND: A Data-Driven Approach and Tool for Mapping Multivariate Data to Color,” IEEE Trans. on Visualization and Computer Graphics,25(2): 1361-1377, 2019. pdf ppt

Funding: NSF grant IIS-1527200 and BNL LDRD grant 16-041