Exploration and Application of Graphic Design Language Based on Artificial Intelligence Visual Communication

Abstract

Graphic design is one of the design disciplines with the longest history in the history of human design. As a marginal discipline, it integrates science and art organically. Graphic design has three basic characteristics: information, art, and economy. Information refers to the fact that graphic design is a carrier of information communication, and graphic design works have the practical function of specific information transmission. Among them, graphic design language is the direct form of information communication, but there is a relative lack of research on the intelligent recognition of graphic design language. To address this problem, this paper focuses on the analysis method of graphic design language based on artificial intelligence visual communication. First, in order to segment different parts of the image more accurately, this paper improves a deep learning-based image segmentation method. The method uses a three-branch network structure to learn semantic information, detail information, and fusion information, respectively. The coding network uses a lightweight convolutional neural network and adds an attention mechanism in the branches to weight the importance of image feature channels, and the features extracted from different perceptual fields of the image are multiscale fused to fuse the features extracted from different stages of the coding network. Then, the salient target regions are detected on the basis of segmented images, the salient target emotions are analyzed by using feature pyramids to improve the convolutional neural network, the emotions expressed by graphic design language are analyzed by constructing a weighted loss convolutional neural network on a multilayer supervised module, and the salient target emotions are fused to obtain the final emotion classification results. The experimental results show that our proposed method outperforms traditional segmentation methods in the dataset, and the sentiment analysis based on segmented images can obtain higher sentiment classification accuracy than the sentiment analysis method that directly identifies the whole image, which is beneficial to the research and application of graphic design language.