Semantic Context Matters: Analysis of Color Names Across Domains
Computer Vision and Pattern RecognitionHuman-Computer InteractionMultimedia
Summary
The authors looked at how people name colors differently depending on the context, like makeup, Crayola crayons, or car colors. They used a special color model called COLIBRI to see how color names fit into 86 fuzzy categories and found that each context uses different parts of this color space. For example, Cosmetics focused on warm colors, Crayola covered a wide range evenly, and Car colors centered on blues and neutral tones. Their work shows that naming colors isn’t just about the actual color but also about where and how the colors are used. This helps in making smarter color-related tools like product search and design systems.
Authors
Adilet Yerkin, Elnara Kadyrgali, Malika Ziyada, Nuray Toganas, Muragul Muratbekova, Ayan Igali, Aruzhan Sabitkyzy, Pakizar Shamoi
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.