Dataset artificial augmentation with a small number of training samples for reflectance estimation.

Abstract

The accuracy of the spectral reflectance estimation approaches highly depends on the amount, coverage, and representation of valid samples in the training dataset. We present a dataset artificial augmentation approach with a small number of actual training samples by light source spectra tuning. Then, the reflectance estimation process is carried out with our augmented color samples for commonly used datasets (IES, Munsell, Macbeth, Leeds). Finally, the impact of the augmented color sample number is investigated using different augmented color sample numbers. The results show that our proposed approach can artificially augment the color samples from CCSG 140 color samples to 13791 color samples and even more. The reflectance estimation performances with augmented color samples are much higher than with the benchmark CCSG datasets for all tested datasets (IES, Munsell, Macbeth, Leeds, as well as a real-scene hyperspectral reflectance database). It indicates that the proposed dataset augmentation approach is practical for improving the reflectance estimation performances.