Abstract:Human color categories are not uniformly distributed in perceptual space, yet most computational color models still assume fixed and evenly structured representations. In this paper, we present a focused analytical extension of the COLIBRI fuzzy color model by investigating perceptual asymmetry between hue categories. Using previously collected large-scale human color categorization data, we introduce quantitative measures of category extent and boundary uncertainty, namely Wideness and Boundary Width, derived from fuzzy membership functions at the {\alpha} = 0.5 level. The analysis reveals a strong imbalance between the two categories: yellow occupies a compact and sharply constrained region of the hue space, whereas green spans a substantially broader interval and exhibits a more extended transition structure. The results show that perceptual color categories are not only fuzzy, but also highly non-uniform in their geometric organization. This asymmetry suggests that some categories behave as narrow, highly specific perceptual labels, while others function as broad, tolerant regions of human color naming. These findings provide a new perspective on linguistic color categorization and extend the interpretability of the COLIBRI framework for perceptually grounded color modeling.
| Comments: | The paper has been submitted for consideration to ICICS 2026 (International Conference on Informatics and Computer Science) |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.09339 [cs.CV] |
| (or arXiv:2605.09339v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2605.09339 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Muragul Muratbekova [view email]
[v1]
Sun, 10 May 2026 05:31:55 UTC (5,542 KB)