基于图像统计的颜色概念关联估计
Estimating Color-Concept Associations from Image Statistics
摘要：
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。当可视化中的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，使调色板在语义上可解释。人们一直在努力优化调色板设计以实现语义可解释性，但这需要对人类颜色概念关联进行良好的估计。从人类那里获得这些数据是昂贵的，这激发了对自动化方法的需求。我们开发和评估了一种新的方法，自动估计颜色概念协会的方式，与人类的评级密切相关。基于之前使用Google图像的研究，我们的方法直接在Google图像搜索结果上操作，而不需要人工参与。具体来说，我们评估了几种方法提取原始像素内容的图像，以最好地估计颜色概念协会从人类的评级。最有效的方法是使用颜色空间中的圆柱扇区和颜色类别的组合来提取颜色。我们证明，我们的方法可以准确地估计平均人类的颜色概念协会不同的水果只使用一小部分图像。该方法还适度地推广到更复杂的回收相关概念的对象，可以出现在任何颜色。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
