Image Matching Technology Based on Improved Particle Swarm Optimization Algorithm
FENG Hao
LI Xianwei
Abstract：Based on the analysis of the basic particle swarm optimization algorithm,the thesis adjusted the learn?ing factor by the strategy of nonlinear asynchronous,changed the model of fixed constant,and balanced the global and local search ability in the process of iteration;simultaneously the thesis introduced the activity factor to im?prove the population diversity which performed mutation for the particles who lost energy.The improved algorithm can improve the global search capability in the multidimensional space,and avoided premature convergence phe?nomenon.The improved particle swarm algorithm was introduced into image matching optimization problem,and proposed an image matching algorithm based on the improved particle swarm algorithm,the experimental results showed that The algorithm has the advantages of faster matching speed and higher matching accuracy,and has strong robustness.
Keywords：particle swarm optimization algorithmimage matchinglearning factoractivity factor
Publication Date：2016-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：4( 22-25 )