Realization of Color Sensibility Using Neural Networks

Recently, “Human Sensibility” is becoming increasingly important in various fields, such as design, human interface, computer graphics, computer music, and playing machines. There are a lot of factors which influence human sensibility. Color, in particular, strongly works on our mind and rouses various images.The purpose of this work is to realize a human sensibility system for colors which extracts some images such as “pleasant-unpleasant” and “warm-cool” from R (red), G (green), and B (blue) values obtained with a TV camera as color information. The system is constructed using a neural network in which neurons are arbitrary connected and consists of two parts. One of them is a sub-network for color vision which is constructed based on its physiological and psychological knowledge. The other is another sub-network for sensibility itself. The neural network learns a human function for color sensibility from teaching data obtained by a sensory test. The outputs of the network after learning are similar to color images that we have in our mind.The following results are proved by the analysis of the hidden units after learning.1. The sub-network for color vision shows similar responses to those of a conventional color vision model.2. The hidden units of the sub-network for sensibility responds selectively to “Hue” and “Tone”. The output units consolidate those selective responses and consequently the network realizes the sensibility for colors.