Visual Mechanism Characteristics of Static Painting Based on PSO-BP Neural Network

Abstract

Static painting works have independent theme significance in the framework of Chinese painting history, and their overall structure, lightness structure, and color structure all show different characteristics of visual mechanism. In order to extract the visual mechanism features effectively, this experiment uses the PSO algorithm to optimize the BP neural network, constructs the PSO-BP neural network for feature recognition and extraction, and compares it with the training results of other algorithms. The results show that the prediction accuracy, recognition accuracy, and ROC curve of PSO-BP neural network are high, which shows that the convergence of PSO-BP neural network is good, and it can effectively complete the recognition and analysis of people and effectively extract the visual mechanism features of static writing paintings.