Toward a Universal Color Naming System: A Clustering-Based Approach using Multisource Data
Summary: arXiv:2604.03235v1 Announce Type: cross
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.
Introduction
In a world where visual communication is paramount, the way we name colors can significantly impact user experience and product design. The absence of a universal color naming system results in inconsistencies that can hinder creativity and efficiency across various sectors. This study aims to tackle this issue by proposing a novel clustering-based multisource data framework that seeks to standardize color naming.
Methodology
Our research involved the collection of a comprehensive dataset comprising over 19,555 RGB values associated with color names from 20 diverse sources. Following data cleaning and normalization, we converted these colors into the perceptually uniform CIELAB color space. To identify optimal color clusters, we employed K-means clustering utilizing the CIEDE2000 color difference metric.
Key Findings
Through our analysis, we identified 280 optimal clusters that effectively represent distinct color categories. For each cluster, we performed a frequency analysis of the associated names, enabling us to assign representative labels that reflect naturally occurring linguistic patterns. The results indicate a significant improvement in the coherence and usability of color naming conventions.
Applications
The proposed color naming system has demonstrated its effectiveness in various applications, particularly in:
- Automatic annotation of images, enhancing the retrieval process in large databases.
- Content-based image retrieval, allowing users to search for images using color-centric queries.
- Generative AI systems, facilitating more accurate color assignments in design tasks.
- Design systems, ensuring a consistent and standardized approach to color usage across platforms.
Conclusion
The development of a universal color naming system represents a significant advancement in how color is understood and communicated across various industries. By leveraging a clustering-based approach using multisource data, we have laid the groundwork for a standardized, perceptually grounded color labeling system. This innovation not only enhances the practical applications of color naming but also opens new opportunities for research and development in areas such as visual search and design. The future of color communication looks promising as we move toward a more unified understanding of color across different domains.