In this paper, we propose a principal color component extraction
method that is simply performed by linear color composition (transformation)
of R, G, B colors, but its composite coefficients are calculated
so as to obtain a noisy-texture-less principal component of
RGB color images. Our method is related to principal component
analysis (PCA) and edge preserving smoothing by total variation
(TV) minimization. The resultant image becomes a principal color
component image with the minimum total variation. We show this
problem can be formulated as TV minimization on a spherical manifold
for a whitened data matrix. Although this spherical constraint is
non-convex, it can be solved by using alternating direction method
of multipliers (ADMM). As its application, we show the results of
text character extraction from ancient wooden tablets, and how our
method extracts faint ink characters while reducing wood grain textures.
Our method is unsupervised but has performance equivalent
to a linear discriminant analysis (LDA) method with user-assisted
information.