Results for applying diferent color-based highlighting methods for highlighting points in a multi-class scatterplot. (a) (top) colorization with Tableau palette and default assignment; (bottom) highlighting efect achieved by assigning a grey color to all non-selected data points; (b) (top) result for a Palettailor-generated palette [21]; (bottom) highlighting achieved by increasing lightness of non-selected data points; (c) (top) colorization by Tableau palette and optimal assignment; (bottom) highlighting by applying Tableau Highlighter function; (d) (top) colorization by our method using a salient color palette; (bottom) highlighting result by combining salient and faint color mapping schemes. Problematic areas are indicated by arrows. Our method allows highlighting a subset of data points while maintaining the discriminability of all non-selected points as well as the color consistency of all color pairs.
Material
Abstract
Color is one of the main visual channels used for highlighting elements of interest in visualization. However, in multi-class scatterplots, color highlighting often comes at the expense of degraded color discriminability. In this paper, we argue for context-preserving highlighting during the interactive exploration of multi-class scatterplots to achieve desired pop-out effects, while maintaining good perceptual separability among all classes and consistent color mapping schemes under varying points of interest. We do this by first generating two contrastive color mapping schemes with large and small contrasts to the background. Both schemes maintain good perceptual separability among all classes and ensure that when colors from the two palettes are assigned to the same class, they have a high color consistency in color names. We then interactively combine these two schemes to create a dynamic color mapping for highlighting different points of interest. We demonstrate the effectiveness through crowd-sourced experiments and case studies.