Hierarchical Chamfer Matching: A Parametric Edge Matching Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Pattern Recognition Letters - Special issue on genetic algorithms
A genetic algorithm for affine invariant recognition of object shapes from broken boundaries
Pattern Recognition Letters
Partial shape matching using genetic algorithms
Pattern Recognition Letters
Swarm intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Pattern Recognition Letters
Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Efficient contour-based shape representation and matching
MIR '03 Proceedings of the 5th ACM SIGMM international workshop on Multimedia information retrieval
Coarse-to-Fine Multiscale Affine Invariant Shape Matching and Classification
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Applying Genetic Algorithms on Pattern Recognition: An Analysis and Survey
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
2D Affine-Invariant Contour Matching Using B-Spline Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Affine invariant matching of broken boundaries based on particle swarm optimization
Image and Vision Computing
Hi-index | 0.00 |
Recently particle swarm optimization (PSO) has been successfully applied in identifying contours that are originated from different views of the same object. As compared with similar approaches based on simple genetic algorithms (SGA), the PSO exhibits higher success rates, faster convergence speed and in general more stable performance. Despite these favorable factors, there are scenarios where the failure rates in matching certain contours are prominently higher than its peers, and the overall performance also deteriorates rapidly with decreasing swarm size. These shortcomings could be attributed to the lack of an initial swarm community which has the quality to reach the global solution. In this paper we first propose a solution to overcome this problem by integrating PSO and the static migrant principle (SMP). The latter is analogous to migrant policy in real life, introducing a fixed and continuous influx of foreign candidates to the swarm community to promote the diversity, and hence the exploration power in the population. Evaluations show that method is less sensitive to the swarm size, and exhibits moderate enhancement in the success rates as compared with the use of PSO alone. To further improve the performance, we introduce the dynamic migrant principle (DMP) to adjust the balance between exploration and exploitation throughout the optimization process. With this approach high success rates are attained for all test samples based on a small swarm community. In addition, the incorporation of both versions of the migrant principle does not impose any overhead on the complexity of the matching scheme.