Painting Classification in Art Teaching under Machine Learning from the Perspective of Emotional Semantic Analysis

Abstract

This paper aims to explore the Painting Classification in art teaching under Machine Learning. Based on Emotional Semantics and Machine Learning, the Emotional Semantics of the traditional image are expounded. Firstly, Emotional Semantics are applied to figure painting in art teaching. Then, the convolutional sparse automatic encoder model is introduced in Painting Classification. Finally, the accuracies of the Painting Classification of the Support Vector Machine classifier (SVMC) and that of the Naive Bayes classifier are compared, and the relevant conclusions are drawn. The accuracy of Painting Classification is positively correlated with the scale of painting. After analysis, the painting set is classified in a ratio of 2 : 1, with 2/3 as training set and 1/3 as test set, which is conducive to the good accuracy of classification. In Machine Learning, proper whitening can improve the accuracy of Painting Classification to a certain extent. However, when the whitening treatment coefficient is selected, it cannot be too large, and the average pooling is more accurate than maximum pooling. After the comparison of the new SVMC, the Naive Bayes classifier, and the convolutional sparse automatic encoder, the convolutional sparse automatic encoder has the highest accuracy of Painting Classification. Therefore, the Painting Classification in art teaching under Machine Learning is explored, which is of great help to the classification work of students or teachers in the future.