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Chromatic structure of natural scenes.
Wachtler T, Lee TW, Sejnowski TJ.
Computational Neurobiology Laboratory, The Salk Institute, La Jolla, California 92037, USA. wachtler@biologie.uni-freiburg.de
We
applied independent component analysis (ICA) to hyperspectral images in
order to learn an efficient representation of color in natural scenes.
In the spectra of single pixels, the algorithm found basis functions
that had broadband spectra and basis functions that were similar to
natural reflectance spectra. When applied to small image patches, the
algorithm found some basis functions that were achromatic and others
with overall chromatic variation along lines in color space, indicating
color opponency. The directions of opponency were not strictly
orthogonal. Comparison with principal-component analysis on the basis
of statistical measures such as average mutual information, kurtosis,
and entropy, shows that the ICA transformation results in much sparser
coefficients and gives higher coding efficiency. Our findings suggest
that nonorthogonal opponent encoding of photoreceptor signals leads to
higher coding efficiency and that ICA may be used to reveal the
underlying statistical properties of color information in natural
scenes.
PMID: 11152005 [PubMed - indexed for MEDLINE]
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