Semantic Context Matters: Analysis of Color Names Across Domains

2026-07-19

AI Summary

Color naming is influenced not only by physical chromatic values but also significantly by semantic context, a nuance inadequately captured by traditional numerical similarity models. This work proposes a context-aware color modeling paradigm and systematically elucidates, for the first time, the mechanistic role of semantic context in human color naming behavior. Leveraging the COLIBRI fuzzy color category space, the study employs metrics including category coverage, Shannon entropy, and maximum lift to conduct a cross-domain analysis across three datasets: cosmetics, Crayola crayons, and automotive colors. The findings reveal distinct contextual biases: Crayola exhibits broad and balanced coverage, cosmetics favor warm hues, and automotive colors cluster predominantly in blue and achromatic regions—collectively underscoring the pivotal role of semantic context in color perception and naming.

Abstract

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.

Problem

• color naming
• semantic context
• context-dependent
• color perception
• human interpretation

Innovation

• semantic context
• color naming
• COLIBRI color model
• context-aware modeling
• fuzzy color categories