Application of Improved PSO-SVM Approach in Image Classification

Support Vector Machines (SVM) are very powerful classifiers in theory but their efficiency in practice rely on an optimal selection of hyper-parameters. This paper proposes an image classifier based on Support Vector Machine which related parameters are optimized by an improved Particle Swarm Optimization (PSO) algorithm. Because control parameters selection of PSO have no corresponding theoretical guidance, most choices are based on experience, this paper proposed a Genetic Algorithm (GA) and PSO hybrid algorithm. In the new algorithm, Genetic Algorithm is employed to select the PSO control parameters dynamically. Experimental results demonstrate that the proposed PSO algorithm outperforms the traditional approaches while selecting optimal parameters for SVM, it has better search capability and improves the accuracy of image classification.