使用机器学习和人类相似性判断的感知颜色差异建模
Perceptual Color Difference Modeling Using Machine Learning and Human Similarity Judgments
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中文总结 AI 辅助
本研究利用人类相似性判断和机器学习，通过比较不同颜色表示和回归算法，发现COLIBRI特征结合LightGBM能最佳预测感知颜色差异，R²达0.703。
中文摘要 AI 辅助
准确评估颜色差异对于从数字设计到质量控制的各类应用至关重要。虽然现有的颜色差异度量（如CIEDE2000）旨在近似人类感知，但它们仍可能与感知判断存在不一致。在本研究中，我们探索了一种基于人类评价直接进行颜色差异估计的数据驱动方法。我们收集了2,000对系统生成的颜色对的相似性判断，每对由七名观察者使用四点有序量表进行评分。这些判断随后被用于训练使用不同颜色表示的回归模型，包括RGB通道差异、HSI差异和COLIBRI模糊语言类别。使用五种回归算法的实验表明，颜色模型的选择对预测性能的影响大于回归算法的选择。仅使用COLIBRI特征时，线性回归的R²达到0.595，优于RGB和HSI表示（其R²值分别为0.479和0.493）。最佳性能由使用组合表示的LightGBM获得，R²达到0.703。结果表明，当数值颜色坐标辅以分级感知类别时，人类感知颜色差异能被更好地捕获，这凸显了数据驱动模型在感知对齐的颜色差异估计中的潜力。
英文摘要
Accurate assessment of color differences is essential for applications ranging from digital design to quality control. While existing color difference metrics, such as CIEDE2000, aim to approximate human perception, they may still exhibit inconsistencies with perceptual judgments. In this study, we investigate a data-driven approach to color-difference estimation based directly on human evaluations. We collect similarity judgments for 2,000 systematically generated color pairs, each rated by seven observers using a four-point ordinal scale. These judgments are then used to train regression models using different color representations, including RGB channel differences, HSI differences, and COLIBRI fuzzy linguistic categories. Experiments with five regression algorithms show that the choice of color model has a greater influence on prediction performance than the choice of regression algorithm. Using COLIBRI features alone, linear regression achieves an R2 of 0.595, outperforming RGB and HSI representations, which achieve R2 values of 0.479 and 0.493, respectively. The best performance is obtained by LightGBM using the combined representation, reaching an R2 of 0.703. The results indicate that human perceptual color differences are better captured when numerical color coordinates are complemented by graded perceptual categories, highlighting the potential of data-driven models for perceptually aligned color-difference estimation.
发表机构
- Kazakh-British Technical University（哈萨克-英国技术大学）
- ADA University（ADA大学）
机构由 AI 辅助整理，请以论文原文为准。
