Bootstrapping Colour Constancy


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 past, 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 algorithm, 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.

Full text (postscript, 553 KB)

Full text (GNU zipped postscript, 152 KB)

Full text (Adobe PDF, 79 KB)

Keywords: color, color correction, color constancy, neural networks

copyright 1998 B.V. Funt, V. Cardei