An improved particle swarm optimization algorithm for high dimension image matching

This paper proposes an optimized and efficient matching method based on Particle Swarm Optimization (PSO) for image matching. PSO is an efficient intelligent algorithm in image matching. It is a kind of stochastic optimized algorithm developed by Eberhart and Kennedy in 1995. In this paper, the application of PSO is focused on image matching in 3 dimensions with variant angles. The ordinary template matching for the 3 dimensions image matching involves large computational complexity. PSO has been improved in the aspect of self-adaption for convergence. Combining PSO with the individual intelligence, the computation and error rate have been significantly reduced. An extended part of PSO algorithm called multi-swarms is introduced. The multi-swarms PSO (MPSO) is applied to the multi-targets matching in the high dimension space. The performance of MPSO is satisfactory due to the interaction between different swarms such as repulsion and convergence. The Experiments results show that Particle Swarm Optimization Algorithm is much faster in the image matching tasks. MPSO has a good performance in multi-targets matching which involves huge computation complexity.