In this paper, with projection value being considered as fitness value, the Dynamic Multi-Swarm Particle Swarm Optimizer (DMS-PSO) is applied to improve the best atom searching problem in the Sparse Decomposition of image based on the Matching Pursuit (MP) algorithm. Furthermore, Discrete Coefficient Mutation (DCM) strategy is introduced to enhance the local searching ability of DMS-PSO in the MP approach over the anisotropic atom dictionary. Experimental results indicate the superiority of DMS-PSO with DCM strategy in contrast with other popular versions of PSO. © 2013 Springer-Verlag.
CITATION STYLE
Chen, C., Liang, J. J., Qu, B. Y., & Niu, B. (2013). Using dynamic multi-swarm particle swarm optimizer to improve the image sparse decomposition based on matching pursuit. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7996 LNAI, pp. 587–595). https://doi.org/10.1007/978-3-642-39482-9_68
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