Predicting Colour Reflectance with Gradient Boosting and Deep Learning

Colour matching remains to be a labour-intensive task which requires a combination of the colourist’s skills and a time consuming trial-and-error process even when employing the standard analytical model for colour prediction called Kubelka-Munk. The goal of this study is to develop a system which can perform an accurate prediction of spectral reflectance for variations of recipes of colourant concentration values, which could be used to assist the colour matching process. In this study we use a dataset of paint recipes which includes over 10,000 colour samples that are mixed from more than 40 different colourants. The framework we propose here is based on a novel hybrid approach combining an analytical model and a Machine Learning model, where a Machine Learning algorithm is used to correct the spectral reflectance predictions made by the Kubelka-Munk analytical model. To identify the optimal Machine Learning method for our hybrid approach, we evaluate several optimised models including Elastic Net, eXtreme Gradient Boosting and Deep Learning. The performance stability of the models are studied by performing computationally intensive Monte Carlo validation. In this work we demonstrate that our hybrid approach based on an eXtreme Gradient Boosting regressor can achieve superior performance in colour predictions, with good stability and performance error rates as low as 0.48 for average
and 1.06 for RMSE.