Bootstrapping color constancy

Brian V. Funt; Vlad C. Cardei

doi:10.1117/12.348463

19 May 1999 Bootstrapping color constancy

Brian V. Funt, Vlad C. Cardei

Proceedings Volume 3644, Human Vision and Electronic Imaging IV; (1999) https://doi.org/10.1117/12.348463
Event: Electronic Imaging '99, 1999, San Jose, CA, United States

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 past, prefect 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.

Citation Download Citation

Brian V. Funt and Vlad C. Cardei "Bootstrapping color constancy", Proc. SPIE 3644, Human Vision and Electronic Imaging IV, (19 May 1999); https://doi.org/10.1117/12.348463

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