# COLIBRI Fuzzy Model: Color Linguistic-Based Representation and Interpretation

arXiv: https://arxiv.org/abs/2507.11488 · HTML: https://mlsanity.com/paper/2507.11488/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 121 references that are in the map.
- First posted on arXiv 15 Jul 2025.

## Builds on (most cited first, 4 of 4)

- [Gromov-Wasserstein unsupervised alignment reveals structural correspondences between the color similarity structures of humans and large language models](https://mlsanity.com/paper/2308.04381/) (2023)
- [Well Googled is Half Done: Multimodal Forecasting of New Fashion Product Sales with Image-based Google Trends](https://mlsanity.com/paper/2109.09824/) (2021)
- [Color-Emotion Associations in Art: Fuzzy Approach](https://mlsanity.com/paper/2311.18518/) (2023)
- [Comparative Analysis Of Color Models For Human Perception And Visual Color Difference](https://mlsanity.com/paper/2406.19520/) (2024)

## Built on by (most cited first, 6 of 6)

- [Color Models in Image Processing: A Review and Experimental Comparison](https://mlsanity.com/paper/2510.00584/) (2025)
- [Perceptual Asymmetry Between Hue Categories: Evidence from Human Color Categorization](https://mlsanity.com/paper/2605.09339/) (2026)
- [Not All Color Categories Are Equally Stable: A Multilingual Free Color Naming Experiment](https://mlsanity.com/paper/2607.10465/) (2026)
- [Beyond Color Geometry: Evaluating Human-Like Color Representations in Vision Models](https://mlsanity.com/paper/2607.13647/) (2026)
- [Semantic Context Matters: Analysis of Color Names Across Domains](https://mlsanity.com/paper/2607.17221/) (2026)
- [Perceptual Color Difference Modeling Using Machine Learning and Human Similarity Judgments](https://mlsanity.com/paper/2609.39130/) (2026)

## Abstract (arXiv)

Colors are omnipresent in today's world and play a vital role in how humans perceive and interact with their surroundings. However, it is challenging for computers to imitate human color perception. This paper introduces the Human Perception-Based Fuzzy Color Model, COLIBRI (Color Linguistic-Based Representation and Interpretation), designed to bridge the gap between computational color representations and human visual perception. The proposed model uses fuzzy sets and logic to create a framework for color categorization. Using a three-phase experimental approach, the study first identifies distinguishable color stimuli for hue, saturation, and intensity through preliminary experiments, followed by a large-scale human categorization survey involving more than 1000 human subjects. The resulting data are used to extract fuzzy partitions and generate membership functions that reflect real-world perceptual uncertainty. The model incorporates a mechanism for adaptation that allows refinement based on feedback and contextual changes. Comparative evaluations demonstrate the model's alignment with human perception compared to traditional color models, such as RGB, HSV, and LAB. To the best of our knowledge, no previous research has documented the construction of a model for color attribute specification based on a sample of this size or a comparable sample of the human population (n = 2496). Our findings are significant for fields such as design, artificial intelligence, marketing, and human-computer interaction, where perceptually relevant color representation is critical.