Generalization procedures for color recognition

It is impossible to collect more than a tiny proportion of all of the possible examples of a given hue, to form a training set for a machine that learns to discriminate colors. In view of this, it is argued that color generalization is essential. A mechanism of learning colors defined by a human being has been developed by A. P. Plummer and is implemented in a commercial device known as the Intelligent Camera. (This device is being used by the authors in association with software written in Prolog. This combination has been described in an earlier publication and is being used in a study of methods for declarative programming of machine vision systems for industrial applications.) The Intelligent Camera can learn the characteristics of colored scenes presented to it. This paper presents four procedures which allow the range of colors learned by the Intelligent Camera to be broadened, so that recognition is made more reliable and less prone to generating noisy images which are difficult to analyze. Three of the procedures can be used to improve color discrimination, while a fourth procedure is used when a single and general color concept has to be learned. Several experiments were devised in an attempt to demonstrate the effectiveness of color generalization. These have shown that it is indeed possible to achieve reliable color discrimination/recognition in situations where it would hitherto have been difficult.