Format

Send to

Choose Destination
See comment in PubMed Commons below
J Opt Soc Am A Opt Image Sci Vis. 2001 Jan;18(1):65-77.

Chromatic structure of natural scenes.

Author information

1
Computational Neurobiology Laboratory, The Salk Institute, La Jolla, California 92037, USA. wachtler@biologie.uni-freiburg.de

Abstract

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
[Indexed for MEDLINE]
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for Optical Society of America
    Loading ...
    Support Center