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Reconstruction of Munsell color space by a five-layered neural network

A wine-glass-type five-layered neural network (81-10-3-10-81) has been constructed, and identity mapping has been realized on the set of surface spectral reflectance data of Munsell color chips by a backpropagation learning algorithm. The network is divided into two parts: encoder (81-10-3) and decoder (3-10-81). Surface spectral reflectance data as physical attributes of color are transformed nonlinearly in each part. After identity mapping learning was completed, the response pattern of the three hidden units in the middle layer was analyzed to obtain the internal representation of color information acquired by self-learning. As a result, it was found that each hidden unit responds to psychological color attributes, that is, one for the value and the other two units for the constant value plane of the Munsell color system which consists of the hue and chroma. The nonlinear analysis method using five-layered neural networks is shown to be an efficient method for elucidating the color information coding mechanisms in the visual system