COLOR CLASSIFICATION BY NEURAL NETWORKS IN GRAPHIC ARTS

This paper presents a hierarchical modular neural network for colour c lassification in graphic arts, capable of distinguishing among very Si milar colour classes. The network performs analysis in a rough to fine fashion, and is able to achieve a high average classification speed a nd a low classification error. In the rough stage of the analysis, clu sters of highly overlapping colour classes are detected Discrimination between such colour classes is performed in the next stage by using a dditional colour information from the surroundings of the pixel being classified. Committees of networks make decisions in the next stage. O utputs of members of the committees are adaptively fused through the B ADD defuzzification strategy or the discrete Choquet fuzzy integral. T he structure of the network is automatically established during the tr aining process. Experimental investigations show the capability of the network to distinguish among very similar colour classes that can occ ur in multicoloured printed pictures. The classification accuracy obta ined is sufficient for the network to be used for inspecting the quali ty of multicoloured prints.