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Proceedings Paper

A Mathematical Model For Representing Patterns And Pattern Classes Using Semantic Nets
Author(s): A. M. Gokeri
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Paper Abstract

A set theoratical model for representing pattern and pattern classes was previously proposed (Gokeri, 1983). In this paper a method for matching the semantic net model of a given pattern with the elements of a set of pattern class models is proposed. Accordingly, for a pattern class a new mathematical model, M, is defined such that M=<P,ψ>, P is a semantic net defining the pattern class and ψ:PxP →[0,1] is a probability function. ψ(fi,fj) may be interpreted as the conditional probability of occurrence of feature Fi with given Fj. Using these values and an empirically developed decision function, Δ , a .1 measure of simiiarity between the model of a pattern class and model of a sample pattern is determined. The Δ- function returns a scalar value in the interval [-1, 1] such that positive values signify similarity. If A=0, no decision can be made regarding the degree of semblance between two semantic net models, and negative values of A indicate no likeness. Finally, a method for modifying the decision function is offered.

Paper Details

Date Published: 17 January 1985
PDF: 5 pages
Proc. SPIE 0521, Intelligent Robots and Computer Vision, (17 January 1985); doi: 10.1117/12.946181
Show Author Affiliations
A. M. Gokeri, Middle East Technical University (Turkey)


Published in SPIE Proceedings Vol. 0521:
Intelligent Robots and Computer Vision
David P. Casasent; Ernest L. Hall, Editor(s)

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