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

TL;DR

This paper introduces a clustering-based multisource data framework to create a standardized, perceptually grounded color naming system, addressing inconsistencies across industries and applications.

Contribution

It presents a novel method combining multisource data, clustering, and perceptual metrics to establish a universal color naming standard.

Findings

Identified 280 optimal color clusters from over 19,500 RGB samples.

Demonstrated improved automatic color annotation and image retrieval.

Reflected natural linguistic patterns in color naming.

Abstract

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.