Innovative Research on Illustration Design Integrating Color Science and Image Processing Technology

Abstract

How to create intelligently and efficiently has become a hot research topic in the field of illustration design. In this paper, starting from the acceptance intention to satisfy users’ favorites and needs, we propose an intelligent analysis method of style based on a deep clustering model and use the categorized style attributes as a reference to disperse designers’ creative thinking. In addition, a semi-supervised semantic segmentation method is introduced to construct an automatic coloring model for sketches, and the automatic coloring function is realized by incorporating the color label recognition process, which further improves the efficiency of designers’ work. The results of clustering analysis of the STL dataset show that the ACC (78.39%), NMI (67.78%), and ARI (62.93%) metrics of the VIT+K-Means model have the largest results compared with other feature extraction methods. Results are superior to other feature extraction methods. Not only that, adding a pseudo-label retraining process for this model further improves the results of the three metrics by 9.13%, 9.97%, and 10.78%, and the visual analysis experiments of clustering clusters also verified the performance enhancement effect. In the comparative analysis of illustration coloring models, the AdvSSL scheme with semi-supervised strategy achieves the best performance in the semantic segmentation task, with an improvement of 4.31% and 2.29% over the SSAN scheme and the S4GCN scheme, respectively. The experts’ coloring evaluation shows that the improved sketch coloring model has the highest results in all four dimensions compared to the traditional Tag2Pix model.

Keywords

• deep clustering
• pseudo-labeling
• sketch auto-coloring
• semi-supervised model
• illustration design
• 03b70