Research on Emotion Analysis of Chinese Literati Painting Images Based on Deep Learning - Zhang, Jie; Duan, Yingjing; Gu, Xiaoqing (Frontiers in Psychology, 2021)
Starting from a pure image perspective, using machine learning emotion analysis methods to research artworks is a new cross-cutting vision in the field of literati painting, and it is also an effective supplement to research from the perspectives of aesthetics, philosophy and history. This paper constructs a literati painting emotion data set at first; then uses five classic deep learning models to test the data set and selects the most suitable model to be improved for literati painting emotion analysis based on accuracy and model characteristics , the final training accuracy rate of the improved model is 54.17% ,finally visualizes the salient feature areas of the picture in the machine vision, analyzes the visualization results, and summarizes the connection law of the picture content of the Chinese literati painting and the emotion that author expressed. This paper validates the possibility of combining deep learning with Chinese cultural research, provides new ideas for the combination of new technology and traditional Chinese literati painting research, and helps people better understand and grasp Chinese cultural spirit and advanced factors.