Comparison of PCA and ICA in color recognition

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Abstract
It has been shown that a large dataset of color spectra can be represented as a linear combination of a few principal spectra. These principal spectra, which form the basis of a vector-subspace, are usually generated by Principal Component Analysis (PCA), the method widely applied to the analysis of spectral data. The objective of the present study was the comparison of PCA and its extenion Independent Component Analysis (ICA). ICA is a statistical signal processing technique, which tries to express measured signals as a linear combination of unknown source signals. Both methods were applied to a set of 1269 reflectance spectra of the chips in the Munsell Book of Color-Matte Finish Collection and a set of 922 reflectance spectra of the samples in the Pantone Color Formula Guide. Several bases with different number of principal spectra were generated. Each Munsell and Pantone basis was used to reconstruct both the Munsell and the Pantone color spectra. The accuracy of the reconstructability was measured mainly by means of color differences (delta) Eab* (CIELAB), but the spectral reconstruction errors were also determined. The dimension of the subspaces leading to a given reconstruction accuracy is discussed in the paper.

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