R. Lenz et al., UNSUPERVISED FILTERING OF COLOR SPECTRA, Journal of the Optical Society of America. A, Optics, image science,and vision., 13(7), 1996, pp. 1315-1324
We describe a class of unsupervised systems that extract features from
databases of reflectance spectra that sample color space in a way tha
t reflects the properties of human color perception. The systems find
the internal weight coefficients by optimizing an energy function. We
describe several energy functions based on second- and fourth-order st
atistical moments of the computed output values. We also investigate t
he effects of imposing boundary conditions on the filter coefficients
and the performance of the resulting systems for the databases with th
e reflectance spectra. The experiments show that the weight matrix for
one of the systems is very similar to the eigenvector system, whereas
the second type of system tries to rotate the eigenvector system in s
uch a way that the resulting filters partition the spectrum into diffe
rent bands. We also show how the system can be forced to use weight ve
ctors with positive coefficients. Systems consisting of positive weigh
t vectors are then approximated with Gaussian quadrature methods. In t
he experimental part of the paper we investigate the properties of thr
ee databases consisting of reflectance spectra. We compare the statist
ical structure of the different databases and investigate how these sy
stems can be used to explore the structure of the space of reflectance
spectra. (C) 1996 Optical Society of America