Learning Perceptual Kernels for Visualization Design
Learning Perceptual Kernels for Visualization Design
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DOI：
10.1109/tvcg.2014.2346978
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发表时间：
2014-12-01
影响因子：
5.2
通讯作者：
Heer, Jeffrey
中科院分区：
文献类型：
--
作者：
Demiralp, Cagatay;Bernstein, Michael S.;Heer, Jeffrey
可视化设计可以受益于对感知的仔细考虑，因为视觉编码变量（例如颜色、形状和大小）的不同分配会影响观看者解释数据的方式。在这项工作中，我们引入了感知内核：从聚合感知判断中得出的距离矩阵。感知内核以可重复使用的形式表示视觉变量之间和内部的感知差异，可直接应用于可视化评估和自动化设计。我们报告了众包实验的结果，以估计谷粒的颜色、形状、大小及其组合。我们分析了使用五种不同判断类型估计的核，包括对之间的李克特评分、序数三元组比较和手动空间排列，并将它们与现有的感知模型进行比较。我们得出收集感知相似性的建议，然后演示如何将生成的内核应用于自动化可视化设计决策。
Visualization design can benefit from careful consideration of perception, as different assignments of visual encoding variables such as color, shape and size affect how viewers interpret data. In this work, we introduce perceptual kernels: distance matrices derived from aggregate perceptual judgments. Perceptual kernels represent perceptual differences between and within visual variables in a reusable form that is directly applicable to visualization evaluation and automated design. We report results from crowdsourced experiments to estimate kernels for color, shape, size and combinations thereof. We analyze kernels estimated using five different judgment types including Likert ratings among pairs, ordinal triplet comparisons, and manual spatial arrangement and compare them to existing perceptual models. We derive recommendations for collecting perceptual similarities, and then demonstrate how the resulting kernels can be applied to automate visualization design decisions.