语义语境很重要：跨领域颜色名称分析
Semantic Context Matters: Analysis of Color Names Across Domains
AI总结：
研究跨领域颜色命名受语义语境影响，通过映射数据集到COLIBRI颜色模型并分析，发现不同领域在颜色空间占据情况不同，语义语境影响颜色命名，所提框架支持相关上下文感知颜色模型开发。
AI中文摘要：
颜色命名不仅受物理颜色值影响，还受颜色使用的语义语境影响。本文通过将来自化妆品、蜡笔和汽车颜色词汇表的颜色名称数据集映射到COLIBRI颜色模型的86个模糊颜色类别上，研究上下文相关的颜色命名。使用类别覆盖率、香农熵和最大提升来分析上下文变化。结果表明，这三种语境在COLIBRI颜色空间中的占据情况不同：化妆品覆盖86个模糊类别中的48个，蜡笔覆盖50个，汽车颜色覆盖40个。结果表明，蜡笔对模糊颜色空间的使用最广泛且最平衡，化妆品主要集中在暖色调区域，汽车颜色在蓝色和无彩色区域更具专业性。这些发现表明，颜色命名不能仅由数字颜色相似性完全解释，语义语境在人类颜色解释中起重要作用。所提出的框架支持为设计分析、产品搜索、推荐系统和以人为本的人工智能开发上下文感知颜色模型。
英文摘要：
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.
