Painting Modeling Language Based on Convolution Neural Networks in Digital Media Art

Abstract

Digital media, as a high-tech method, has gradually affected all aspects of social production, including artistic creation, in the information age. Contemporary painting art, as an important form of artistic expression, will inevitably be baptized by digital media in the tide of time. The color of digital media ushers in a new era in contemporary painting. The development of deep learning makes the research field of artificial intelligence move towards a deeper and more practical level. Among them, CNN (Convolutional Neural Network) has very important research value in image feature expression. The digitization of painting works is of great significance to the effective use of painting resources. The traditional image classification methods do not consider the subjective characteristics of painting works, and most of the features need to be extracted manually, resulting in the loss of detailed features. The research and design of image retrieval system in this paper is based on CNN. One is to extract the features of the image using convolution neural network. The scale of image data sets in the resource database is still small. For the field of deep learning research, data sets can only be regarded as small-scale data sets. Combined with the actual situation, the convolution neural network model is trained, and the better convolution neural network structure Artnet is selected through experimental analysis. It is a seven-layer network structure including two volume layers, two pool layers, and a full connection layer. The second is to design and implement a practical image retrieval system based on Artnet and effectively integrate it with other systems of the project as an important function of the project construction.