SCENE RECOGNITION USING GENETIC ALGORITHMS WITH SEMANTIC NETS

被引：36

作者：

ANKENBRANDT, CA

BUCKLES, BP

PETRY, FE

机构：

[1] Center for Intelligent and Knowledge-based Systems, Department of Computer Science, Tulane University, New Orleans, LA 70118

来源：

PATTERN RECOGNITION LETTERS | 1990年 / 11卷 / 04期

关键词：

feature labelling; fitness functions; Genetic algorithms; semantic nets;

D O I：

10.1016/0167-8655(90)90067-C

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A model for genetic algorithms with semantic nets is derived for which the relationships between concepts is depicted as a semantic net. An organism represents the manner in which objects in a scene are attached to concepts in the net. Predicates between object pairs are continuous valued truth functions in the form of an inverse exponential function (e-β|x|). 1 : n relationships are combined via the fuzzy OR (Max[...]). Finally, predicates between pairs of concepts are resolved by taking the average of the combined predicate values of the objects attached to the concept at the tail of the arc representing the predicate in the semantic net. The method is illustrated by applying it to the identification of oceanic features in the North Atlantic. © 1990.

引用

页码：285 / 293

页数：9

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