Research Progress on the Aesthetic Quality Assessment of Complex Layout Images Based on Deep Learning

被引:1
|
作者
Pu, Yumei [1 ]
Liu, Danfei [1 ]
Chen, Siyuan [1 ]
Zhong, Yunfei [1 ]
机构
[1] Hunan Univ Technol, Sch Packaging & Mat Engn, Zhuzhou 412007, Peoples R China
来源
APPLIED SCIENCES-BASEL | 2023年 / 13卷 / 17期
关键词
deep learning; image aesthetic evaluation; layout analysis; image segmentation; DOCUMENT STRUCTURE; NEURAL-NETWORK; PHOTO; CLASSIFICATION; DESIGN;
D O I
10.3390/app13179763
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
With the development of the information age, the layout image is no longer a simple combination of text and graphics, but covers the complex layout image obtained from text, graphics, images and other layout elements through the process of artistic design, pre-press processing, typesetting, and so on. At present, the field of aesthetic-quality assessment mainly focuses on photographic images, and the aesthetic-quality assessment of complex layout images is rarely reported. However, the design of complex layout images such as posters, packaging labels, advertisements, etc., cannot be separated from the evaluation of aesthetic quality. In this paper, layout analysis is performed on complex layout images. Traditional and deep-learning-based methods for image layout analysis and aesthetic-quality assessment are reviewed and analyzed. Finally, the features, advantages and applications of common image aesthetic-quality assessment datasets and layout analysis datasets are compared and analyzed. Limitations and future perspectives of aesthetic assessment of complex layout images are discussed in relation to layout analysis and aesthetic characteristics.
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页数:20
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