# Toward a Universal Color Naming System: A Clustering-Based Approach using Multisource Data

arXiv: https://arxiv.org/abs/2604.03235 · HTML: https://mlsanity.com/paper/2604.03235/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 43 references that are in the map.
- First posted on arXiv 30 Jan 2026.

## Builds on (most cited first, 6 of 6)

- [Controllable Text-to-Image Generation](https://mlsanity.com/paper/1909.07083/) (2019)
- [Generative AI](https://mlsanity.com/paper/2309.07930/) (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)
- [Comparative Analysis Of Color Models For Human Perception And Visual Color Difference](https://mlsanity.com/paper/2406.19520/) (2024)
- [Color Models in Image Processing: A Review and Experimental Comparison](https://mlsanity.com/paper/2510.00584/) (2025)
- [Color and Sentiment: A Study of Emotion-Based Color Palettes in Marketing](https://mlsanity.com/paper/2407.16064/) (2024)

## Built on by (most cited first, 1 of 1)

- [Not All Color Categories Are Equally Stable: A Multilingual Free Color Naming Experiment](https://mlsanity.com/paper/2607.10465/) (2026)

## Abstract (arXiv)

Is it coral, salmon, or peach? What seems like a simple color can have many names, and without a standard, these variations create confusion across design, technology, and communication. Color naming is a fundamental task across industries such as fashion, cosmetics, web design, and visualization tools. However, the lack of universally accepted color naming standards leads to inconsistent color standards across platforms, applications, and industries. Moreover, these systems include hundreds or thousands of overlapping, perceptually indistinct shades, despite the fact that humans typically distinguish only a limited number of unique color categories in practice. In this study, we propose a clustering-based multisource data framework to build a standardized color-naming system. We collected a dataset of over 19,555 RGB values paired with color names from 20 diverse sources. After data cleaning and normalization, we converted the colors to the perceptually uniform CIELAB color space and applied K-means clustering using the CIEDE2000 color difference metric, identifying 280 optimal clusters. For each cluster, we performed a frequency analysis of the associated names to assign representative labels. The resulting system reflects naturally occurring linguistic patterns. We demonstrate its effectiveness in automatic annotation and content-based image retrieval on a clothing dataset. This approach opens new opportunities for standardized, perceptually grounded color labeling in practical applications such as generative AI, visual search, and design systems.