迈向通用颜色命名系统：基于聚类的多源数据方法
Toward a Universal Color Naming System: A Clustering-Based Approach using Multisource Data
AI总结：
本文提出基于聚类的多源数据框架，构建标准化颜色命名系统，通过清洗和归一化19555个RGB值及颜色名称数据，采用CIEDE2000色差度量进行K-means聚类，生成280个最优簇，并通过频率分析分配代表性标签，提升自动标注和图像检索效果。
摘要：
什么是珊瑚、鲑鱼或桃子？看似简单的颜色却有多种名称，没有标准的话，这些变体会在设计、技术和沟通中造成混淆。颜色命名是时尚、化妆品、网页设计和可视化工具等行业的基本任务。然而，缺乏普遍接受的颜色命名标准导致不同平台、应用和行业之间的颜色标准不一致。此外，这些系统包含数百甚至数千种重叠、在感知上不明显的色调，尽管人类实际上通常只能区分有限数量的独特颜色类别。在本研究中，我们提出了一种基于聚类的多源数据框架，以构建标准化的颜色命名系统。我们收集了来自20个不同来源的超过19,555个RGB值和颜色名称的数据集。在数据清洗和标准化之后，我们将颜色转换为感知上均匀的CIELAB颜色空间，并使用CIEDE2000颜色差异度量进行K-means聚类，确定了280个最优簇。对于每个簇，我们对相关名称进行了频率分析，以分配代表性标签。最终系统反映了自然发生的语言模式。我们展示了其在服装数据集上的自动标注和基于内容的图像检索效果。这种方法为在生成式AI、视觉搜索和设计系统等实际应用中实现标准化、基于感知的颜色标记开辟了新机会。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
