Partial Shape Recognition: A Landmark-Based Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
A Graduated Assignment Algorithm for Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern recognition using evolution algorithms with fast simulated annealing
Pattern Recognition Letters
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic-based search for error-correcting graph isomorphism
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Inexact graph matching using a genetic algorithm for image recognition
Pattern Recognition Letters
Affine invariant matching of broken boundaries based on particle swarm optimization
Image and Vision Computing
ARG Based on Arcs and Segments to Improve the Symbol Recognition by Genetic Algorithm
Graphics Recognition. Recent Advances and New Opportunities
Pattern Recognition Letters
Journal of Signal Processing Systems
Journal of Visual Communication and Image Representation
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In this paper, we investigate the genetic algorithm based optimization procedure for structural pattern recognition in a model-based recognition system using attributed relational graph matching technique. In this study, potential solutions indicating the mapping between scene and model vertices are represented by integer strings. The test scene may contain multiple occurrences of different or the same model object. Khoo and Suganthan [Proc. IEEE Congr. Evolutionary Comput. Conf. 2001, p. 727] proposed a solution string representation scheme for multiple mapping between a test scene and all model objects and with the uniform crossover operator. In this paper, we evaluate this proposed solution string representation scheme with another representation scheme commonly used to solve the problem. In addition, a comparison between the uniform, one-point and two-point crossover operators was made. An efficient pose-clustering algorithm is used to eliminate any wrong mappings and to determine the presence/pose of the model in the scene. Simulations are carried out to evaluate the various solution representations and genetic operators.