Utilizing support vector and kernel ridge regression methods in spectral reconstruction

Abstract

Two regression methods, namely, Support Vector Regression (SVR) and Kernel Ridge Regression (KRR), are used to reconstruct the spectral reflectance curves of samples of Munsell dataset from the corresponding CIE XYZ tristimulus values. To this end, half of the samples (i.e., the odd ones) were used as training set while the even samples left out for the evaluation of reconstruction performances. Results were reviewed and compared with those obtained from Principal Component Analysis (PCA) method, as the most common context-based approach. The root mean squared error (RMSE), goodness fit coefficient (GFC), and CIE LAB color difference values between the actual and reconstruct spectra were reported as evaluation metrics. However, while both SVR and KRR methodologies provided better spectral and colorimetric performances than the classical PCA method, the computation costs were considerably longer than PCA method.