IEEE Trans Vis Comput Graph. 2020 Jan;26(1):1226-1235. doi: 10.1109/TVCG.2019.2934536. Epub 2019 Aug 22.
To interpret the meanings of colors in visualizations of categorical information, people must determine how distinct colors correspond to different concepts. This process is easier when assignments between colors and concepts in visualizations match people's expectations, making color palettes semantically interpretable. Efforts have been underway to optimize color palette design for semantic interpretablity, but this requires having good estimates of human color-concept associations. Obtaining these data from humans is costly, which motivates the need for automated methods. We developed and evaluated a new method for automatically estimating color-concept associations in a way that strongly correlates with human ratings. Building on prior studies using Google Images, our approach operates directly on Google Image search results without the need for humans in the loop. Specifically, we evaluated several methods for extracting raw pixel content of the images in order to best estimate color-concept associations obtained from human ratings. The most effective method extracted colors using a combination of cylindrical sectors and color categories in color space. We demonstrate that our approach can accurately estimate average human color-concept associations for different fruits using only a small set of images. The approach also generalizes moderately well to more complicated recycling-related concepts of objects that can appear in any color.
为了解释分类信息可视化中颜色的含义，人们必须确定不同的颜色如何对应不同的概念。当可视化中颜色和概念之间的分配符合人们的期望时，这个过程会更容易，从而使调色板具有语义可解释性。人们一直在努力优化调色板设计以实现语义可解释性，但这需要对人类的颜色-概念关联有很好的估计。从人类那里获取这些数据成本很高，这促使人们需要开发自动化方法。我们开发并评估了一种新方法，以自动估计颜色-概念关联，这种方法与人类的评分具有很强的相关性。基于先前使用 Google 图像的研究，我们的方法直接在 Google 图像搜索结果上运行，而不需要人类参与。具体来说，我们评估了几种从图像中提取原始像素内容的方法，以便最好地估计从人类评分中获得的颜色-概念关联。最有效的方法使用圆柱扇形和颜色空间中的颜色类别组合来提取颜色。我们证明，我们的方法仅使用一小部分图像就可以准确估计不同水果的平均人类颜色-概念关联。该方法对于可以呈现任何颜色的更复杂的与回收相关的物体概念也能适度泛化。
