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