Genetic Algorithms for Training Data and Polynomial Optimization in Colorimetric Characterization of Scanners

Generalization is an important issue in colorimetric characterization of devices. We propose a framework based on Genetic Algorithms to select training samples from large datasets. Even though the framework is general, and can be used in principle for any dataset, we use two well known datasets as case studies: training samples are selected from the Macbeth ColorCheckerDC dataset and the trained models are tested on the Kodak Q60 photographic standard dataset. The presented experimental results show that the proposed framework has better, or at least comparable, performances than a set of other computational methods defined so far for the same goal (Hardeberg, Cheung, CIC and Schettini). Even more importantly, the proposed framework has the ability to optimize the training samples and the characterizing polynomial's coefficients at the same time.