Relational Graph Matching Using Genetic Algorithms

摘要

This paper presents a genetic algorithm (GA) based optimization procedure for pattern recognition using attributed relational graph (ARG) matching technique. In this study potential solutions are represented by integer strings. The population is initialized randomly. A pose clustering algorithm is used to eliminate incompatible mappings in the generated solution and to estimate pose parameters. We present experimental results to demonstrate the procedure.