Application of Intelligent Image Color Technology in Teaching Chinese Painting Color

Abstract

This paper presents an in-depth analysis and research on the application of intelligent image color technology in teaching Chinese painting color. A feature reorganization-based approach to Chinese painting image style migration is proposed. Using the connection between depth features in neural networks and image semantics, the artistic style expression method of traditional Chinese painting is combined with existing methods to match the feature matrix in the generative network by feature reorganization, and finally, the migrated image is generated using a decoder. The texture of the target image is input to the generator as an additional condition, and the discriminator is changed to a relativistic discriminator to build a new generative adversarial network. To demonstrate the effectiveness of the method, a large number of Chinese ink and wash style landscape paintings and natural landscape photos are used as experimental data. The experimental results show that the obtained generated images after style migration are 8% better in realism and quality. An image color intelligent analysis system was designed, which integrates a color classification module, a pigment analysis module, an output saving module, and a pigment sample library to achieve the automatic classification of different color areas in Chinese painting, and ultimately allows a judgment to be made on the most likely pigment to be used for each type of color. In the teaching of Chinese painting, the system can transform the traditional “ear-listening” color transmission through vision into eye perception, which makes the teaching of Chinese painting vivid, arouses the enthusiasm of the classroom, and greatly promotes the color teaching of Chinese painting.