Foundations of genetic algorithms
Foundations of genetic algorithms
Multiple graph matching with Bayesian inference
Pattern Recognition Letters - special issue on pattern recognition in practice V
Graph Matching With a Dual-Step EM Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Use of random graph parsing for scene labelling by probabilistic relaxation
Pattern Recognition Letters
Graph matching with hierarchical discrete relaxation
Pattern Recognition Letters
2000 Congress on Evolutionary Computation
2000 Congress on Evolutionary Computation
Structural Matching in Computer Vision Using Probabilistic Relaxation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition Letters
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 12 - Volume 12
An overview of evolutionary algorithms in multiobjective optimization
Evolutionary Computation
Median graphs: A genetic approach based on new theoretical properties
Pattern Recognition
A graph matching method and a graph matching distance based on subgraph assignments
Pattern Recognition Letters
A polynomial algorithm for submap isomorphism of general maps
Pattern Recognition Letters
Learning graph prototypes for shape recognition
Computer Vision and Image Understanding
Isomorphism Testing via Polynomial-Time Graph Extensions
Journal of Mathematical Modelling and Algorithms
Partial retrieval of CAD models based on the gradient flows in Lie group
Pattern Recognition
Journal of Visual Communication and Image Representation
Social networks profile mapping using games
WebApps'12 Proceedings of the 3rd USENIX conference on Web Application Development
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Exact graph matching using a genetic algorithm for image recognition has been introduced in previously published work. The algorithm was based on angle matching between two given graphs. It has proven to be quite effective in exact graph matching. However, the algorithm needs some modifications in order to handle cases where the number of nodes, shapes and rotations of the two graphs are different. This paper presents modifications such as the introduction of node exemption, inexact matching between straight lines and curves in the graphs and consideration of rotational degrees of the graphs. Each angle in a graph is also given a weight to indicate the significant degree of identifying the graph. A multi-objective function is used to reflect the similarity between two graphs. The experiments designed to evaluate the algorithm have shown very promising results. It is highly accurate in matching graphs with dissimilarities in shape, number of nodes and degrees of rotation.