Visual Memory Neural Network for Artistic Graphic Design

Abstract

Artistic graphic design is the aesthetic result of the designer’s fusion of various elements, with a high degree of independence. Considering the lack of significant visual design scope and aesthetic indicators of graphic design, our research aims to build an upgraded network model that can categorize different types of artistic graphics with labels and realize the free combination of graphic solutions. We realize the scheme reorganization of artistic graphic design from the perspective of computer vision and propose the artistic graphic design method based on memory neural network. We built a computer vision environment and reconstructed the computer vision network to set up an independent deep camera vision range calculation law. Considering the artistic graphic region segmentation problem, we propose the self-attentive mechanism, which can quantitatively segment different artistic graphic regions according to temporal features, before arranging them in a sequence to obtain the graphic region feature vector. We also add the LSTM structure based on the attention mechanism to match with the self-attention features of the graphical region segmentation module and pass the matched attention feature vector to the LSTM network to extract the labeled text feature information of the graphs. To test the effectiveness of our method, we build a database of artistic graphics and set up an adaptive training process. We also compared deep learning methods of the same type, and the experimental results proved that our method outperformed other deep methods in artistic graphic design by keeping the scheme reorganization accuracy and quantitative evaluation of artistic models above 90%.