不同领域用色命名差异大，语义上下文影响人类对颜色的感知。
Semantic Context Matters: Analysis of Color Names Across Domains
- 将美妆、蜡笔、汽车三类颜色名称映射到COLIBRI色彩模型分析上下文差异。
- 蜡笔覆盖50个模糊色域，最广且均衡；美妆集中于暖色调；汽车偏蓝和无彩色。
- 研究为设计分析、推荐系统等提供语义感知色彩建模新思路。
颜色命名不仅受物理色彩值影响，还受使用语境制约。本文通过将美妆、Crayola蜡笔和汽车颜色名称数据集映射至COLIBRI色彩模型的86个模糊色类别，分析上下文依赖的颜色命名差异。采用类别覆盖率、香农熵与最大提升度进行评估。结果表明：美妆覆盖48个类别，蜡笔覆盖50个，汽车覆盖40个。蜡笔在模糊色空间中分布最广且最均衡，美妆主要集中于暖色调区域，汽车则更专注蓝调及无彩色区域。研究显示，仅靠数值相似性无法完全解释颜色命名，语义上下文在人类颜色认知中起关键作用。所提框架有助于推动面向设计分析、产品搜索、推荐系统及以人为中心的人工智能的语义感知色彩模型发展。
原文摘要 · Abstract (English)
Color naming is influenced not only by physical color values but also by the semantic context in which colors are used. This paper investigates context-dependent color naming by mapping color-name datasets from Cosmetics, Crayola, and Car-color vocabularies onto the 86 fuzzy color categories of the COLIBRI color model. Contextual variation is analyzed using category coverage, Shannon entropy, and maximum lift. The results show that the three contexts occupy the COLIBRI color space differently: Cosmetics covers 48 of 86 fuzzy categories, Crayola covers 50, and Car colors cover 40. The results demonstrated that Crayola provides the broadest and most balanced use of the fuzzy color space, Cosmetics is mainly concentrated around warm-tone regions, and Car colors are more specialized around blue and achromatic regions. These findings show that color naming cannot be fully explained by numerical color similarity alone and that semantic context plays an important role in human color interpretation. The proposed framework supports the development of context-aware color models for design analytics, product search, recommendation systems, and human-centered artificial intelligence.
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