Principles of conceptual recognition by neural networks
Sergey Khachatur Aityan · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1992
The ability of the neural networks to recognize patterns which have never been presented to the network before is based on comparison of an applied pattern with the patterns stored by training as the references. The presentation of the patterns provides generation of the image features regions in the features space to which the investigated patterns are related. The feature space is associated with the concepts assigned to the pattern features that had been learned by training. Thus the recognition is a classification over the image concepts. The concepts are being assigned to the pattern by stepwise pattern analysis in conceptualizing depth as well as in resolution depth. Every step of analysis provides new conceptual information that corrects the results of the previous steps. The analysis is based on the pattern-pattern hierarchy function, pattern-concept association, and concept-concept associations. The process is controlled by the neural knowledge base that has been learned with the conceptual associations as the rules. Two types of the concepts partial orders are introduced; `concept C1 is identified AS concept C2' and `concept C1 consists OF concept C2.' The partial orders AS and OF define the hierarchy of concepts in the pattern.