A novel approach to color normalization using neural network

Color is a powerful descriptor that often simplifies object extraction and identification, and many computer vision systems use color to aid object recognition. However, image colors strongly depend on lighting geometry (direction and intensity of light source) and illuminant color (spectral power distribution). Either small variation in the intensity or the change of scene illumination can dramatically make object color changed. To overcome the lighting dependency problem, a color constancy or normalization algorithm should be used for preprocessing. This paper presents a novel approach to performing color normalization. A nonlinear mapping function is estimated using a neural network. Once the mapping function is found accurately, an image under unknown illumination may be transformed to the image under the predetermined illumination, which will be useful for color image processing. Three groups of experiments were conducted. In our experiments, images are processed by various neural networks and the performance is boosted by using a committee machine, and then the mapping errors are estimated and the results are compared with those of other algorithms. The experimental results demonstrate that the performance of the proposed method is superior to that of other color normalization algorithms.