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

被引：2

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.

引用

页数：20

共 50 条

[31] DEEP LEARNING FOR OBJECTIVE QUALITY ASSESSMENT OF 3D IMAGES [J].

Mocanu, Decebal Constantin ;

Exarchakos, Georgios ;

Liotta, Antonio .

2014 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), 2014, :758-762

[32] About the generalizability of deep learning based image quality assessment in mammography [J].

Faller, Josua ;

Amanova, Narbota ;

van Engen, Ruben ;

Martin, Joerg ;

Elster, Clemens .

MACHINE LEARNING-SCIENCE AND TECHNOLOGY, 2023, 4 (04)

[33] Research Progress of Image Inpainting Methods Based on Deep Learning [J].

Chen, Wenxiang ;

Tian, Qichuan ;

Lian, Lu ;

Zhang, Xiaohang ;

Wang, Haoji .

Computer Engineering and Applications, 2024, 60 (22) :58-73

[34] Research Progress and Prospect of Adaptive Optics Based on Deep Learning [J].

Hu, Yiwen ;

Liu, Xin ;

Kuang, Cuifang ;

Liu, Xu ;

Hao, Xiang .

CHINESE JOURNAL OF LASERS-ZHONGGUO JIGUANG, 2023, 50 (11)

[35] A Deep Learning Approach for Assessment of Breast Aesthetic Score and Keypoint Localization [J].

Park, Jin Byeong ;

Kim, Myeongju ;

Park, Joseph Kyu-Hyung ;

Myung, Yujin .

2024 IEEE 48TH ANNUAL COMPUTERS, SOFTWARE, AND APPLICATIONS CONFERENCE, COMPSAC 2024, 2024, :1552-1553

[36] Deep learning for brain disorder diagnosis based on fMRI images [J].

Yin, Wutao ;

Li, Longhai ;

Wu, Fang-Xiang .

NEUROCOMPUTING, 2022, 469 :332-345

[37] Lentil plant disease and quality assessment: A detailed dataset of high-resolution images for deep learning research [J].

Mahamud, Eram ;

Assaduzzaman, Md ;

Sharmin, Shayla .

DATA IN BRIEF, 2025, 58

[38] Research on Urine Sediment Images Recognition Based on Deep Learning [J].

Ji, Qingbo ;

Li, Xun ;

Qu, Zhiyu ;

Dai, Chong .

IEEE ACCESS, 2019, 7 :166711-166720

[39] RESEARCH on target detection of SAR images based on deep learning [J].

Zhu Weigang ;

Zhang Ye ;

Qiu Lei ;

Fan Xinyan .

IMAGE AND SIGNAL PROCESSING FOR REMOTE SENSING XXIV, 2018, 10789

[40] Aesthetic Evaluation of Food Plate Images using Deep Learning [J].

Mikhailava, Veranika ;

Pyshkin, Evgeny ;

Klyuev, Vitaly .

2020 22ND INTERNATIONAL CONFERENCE ON ADVANCED COMMUNICATION TECHNOLOGY (ICACT): DIGITAL SECURITY GLOBAL AGENDA FOR SAFE SOCIETY!, 2020, :285-289

← 1 2 3 4 5 →