IEEE Trans Vis Comput Graph - Perceptually-Driven Visibility Optimization for Categorical Data Visualization.

Tópicos

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{ method(2212) result(1239) propos(1039) }

Resumo

Visualization techniques often use color to present categorical differences to a user. The selection of a color palette needs careful considerations of perceptual qualities of color. Large coherent groups visually suppress smaller groups, and thus, are often visually dominant in images. This article introduces the concept of class visibility to quantitatively measure the utility of a color palette to present coherent categorical structure to the user. We present a color optimization algorithm based on our class visibility metric to make categorical differences clearly visible to the user. We performed two user experiments, a user preference and visual search experiment, to validate our visibility measure over a range of color palettes. The results indicate that visibility is a robust measure, and our color optimization can increase the effectiveness of categorical data visualizations.

Resumo Limpo

visual techniqu often use color present categor differ user select color palett need care consider perceptu qualiti color larg coher group visual suppress smaller group thus often visual domin imag articl introduc concept class visibl quantit measur util color palett present coher categor structur user present color optim algorithm base class visibl metric make categor differ clear visibl user perform two user experi user prefer visual search experi valid visibl measur rang color palett result indic visibl robust measur color optim can increas effect categor data visual

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