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Bootstrapping Color Constancy

Resource type
Date created
1999-01
Authors/Contributors
Author: Funt, Brian
Abstract
Bootstrapping provides a novel approach to training a neural network to estimate the chromaticity of the illuminant in a scene given image data alone. For initial training, the network requires feedback about the accuracy of the network’s current results. In the case of a network for color constancy, this feedback is the chromaticity of the incident scene illumination. In the past1, perfect feedback has been used, but in the bootstrapping method feedback with a considerable degree of random error can be used to train the network instead. In particular, the grayworld algorithm2, which only provides modest color constancy performance, is used to train a neural network which in the end performs better than the grayworld algorithm used to train it.
Document
Description
Presented at the Electronic Imaging IV conference, Jan. 1999.
Published as
Funt, B., and Cardei, V.C., "Bootstrapping Color Constancy," Proc. SPIE Vol. 3644 Electronic Imaging IV, Jan. 1999.
Publication title
Proc. SPIE Electronic Imaging IV
Document title
Bootstrapping Color Constancy
Date
1999
Volume
3644
Copyright statement
Copyright is held by the author(s).
Scholarly level
Peer reviewed?
Yes
Language
English
Member of collection

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