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Pictorial knowledge representation using pictorial semantic networks

Published online by Cambridge University Press:  09 March 2009

Edward T. Lee
Edward T. Lee
Affiliation:
Department of Electrical & Computer Engineering, University of Miami, Coral Gables, Florida 33142 (USA)
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Summary

Classifications of pictures and pictorial knowledge are presented. Pictorial knowledge is divided into three classes – angular pictorial knowledge, side pictorial knowledge, and angular and side pictorial knowledge. A block diagram of these three pictorial knowledge classes and a pictorial knowledge transformation module is also presented with illustrative examples. Pictorial semantic networks which in terms of pictorial nodes, property nodes, “is a” links, “has property” links, and “if and only if” links are introduced. Transitivity, generalization, specialization, inheritance hierarchy, and knowledge transformation properties are stated and illustrated by examples. Triangular, quadrangular, and polygonal knowledge representation using pictorial semantic networks are presented. The concepts of deducible property nodes are also presented with illustrative examples. Additional facts can be established from pictorial semantic networks. Thus, pictorial semantic networks are a useful way to represent pictorial knowledge in domains that use well-established taxonomies to simplify problem solving in pictorial information systems. Pictorial semantic networks offer what appears to be a fertile field for future study. The results may have useful applications in knowledge representation, expert systems, artificial intelligence, knowledge - based systems, pictorial information systems and related areas.

Keywords

SemanticNetworkPictures
Type
Research Article
Information
Robotica , Volume 6 , Issue 2 , April 1988 , pp. 155 - 160
Copyright
Copyright © Cambridge University Press 1988

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