Optimization and Simulation of Garden Image Visual Effect Based on Particle Swarm and Wavelet Threshold

Abstract

This paper optimizes and simulates the visual effects of garden graphics based on particle swarm and wavelet threshold algorithm, and uses deep belief networks as the main body of the classifier. To make up for the shortcomings of the activation function of the deep belief network, this paper adopts the wavelet basis function as the activation function of the deep belief network to improve the recognition accuracy of the deep belief network, especially the recognition accuracy of small changes in the garden image information. It analyzes the learning process of deep belief network in detail, finds out the shortcomings of traditional algorithms, and optimizes them, combined with particle swarm optimization algorithm to construct a wavelet deep belief network model to further improve the recognition accuracy and recognition speed of garden image recognition. A hybrid optimization algorithm of genetic and particle swarm algorithms is proposed. The two algorithms complement each other and the idea of crossover and variation is introduced into the standard Particle Swarm Optimization (PSO) algorithm to avoid premature convergence of solutions obtained by the PSO algorithm and lead to local optimal solutions. Seven optimal solutions obtained by the algorithm are used as the weight of the sample features, which are multiplied by the sample features to obtain the input data of the weighted K-nearest neighbor algorithm. After that, a three-fold cross-validation method is utilized to train the data to ensure the classification effect of the data set. The weighted K-nearest neighbor algorithm has the best classification effect based on the hybrid optimization of genetic algorithms and particle swarm algorithm.