Enhanced affine invariant matching of broken boundaries based on particle swarm optimization and the dynamic migrant principle

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.