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arXiv Computer Vision By Elnara Kadyrgali, Muragul Muratbekova, Adilet Yerkin, Nuray Toganas, Ayan Igali, Malika Ziyada, Aruzhan Burambekova, Jamaladdin Hasanov, Pakizar Shamoi

Perceptual Color Difference Modeling Using Machine Learning and Human Similarity Judgments

Read the original on arXiv Computer Vision →

The paper presents a data‑driven method for estimating perceptual color differences by training regression models on human similarity judgments of 2,000 color pairs. Using COLIBRI fuzzy linguistic categories as features, linear regression achieves an R² of 0.595, outperforming RGB and HSI representations. The best result, an R² of 0.703, is obtained with LightGBM on a combined representation, showing that graded perceptual categories improve color‑difference prediction.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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