Bootstrapping Color Constancy

Peer reviewed: 
Yes, item is peer reviewed.
Scholarly level: 
Faculty/Staff
Final version published as: 

Funt, B., and Cardei, V.C., "Bootstrapping Color Constancy," Proc. SPIE Vol. 3644 Electronic Imaging IV, Jan. 1999.

Date created: 
1999-01
Keywords: 
Color constancy
Neural networks
Color correction
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.

Description: 

Presented at the Electronic Imaging IV conference, Jan. 1999.

Language: 
English
Document type: 
Conference presentation
Rights: 
Rights remain with the authors.
File(s): 
Statistics: