Department of Computer Science and Electrical Engineering
Genetic Algorithm for Pattern Recognition
Daniel Kennedy
Abstract:
This thesis is an investigation into the application of a genetic
algorithm to the task of model and scene based pattern recognition. The
genetic algorithm is used to locate instances of model graphs embedded in
within scene graphs for several different classes of recognition problem.
Input to the system consists of two picture graphs constructed from a
single kind of primitive. In these experiments the primitives used to
construct model and scene picture graphs are spheres, line segments, and the
corner points of polygon silhouettes. An attributed relational graph, which
describes the scene with a vector of attributes for each primitive and a
vector of relationships for each pair of primitives is extracted from both
the model and the scene.
The genetic algorithm that this thesis is a study of determines the
mapping of model nodes to scene nodes which results in the best overall
match between model attribute vectors and the scene attribute vectors to
which they are mapped. A distance measure is developed to compare attributed
relational graphs numerically and extract a cost of mapping. The genetic
algorithm uses strings of integers representing mappings as the population,
the distance measure as a fitness function, and generic genetic operators to
optimise the mapping between model and scene graphs.
Complete thesis:
thesis.pdf
Conference paper:
paper.pdf
About the Author
Dept of Computer Science and Electrical
Engineering / Daniel Kennedy
/ kenneddr@csee.uq.edu.au
last mod 03/09/98