CLC number: TP391
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2020-06-19
Cited: 0
Clicked: 5992
Citations: Bibtex RefMan EndNote GB/T7714
Wei-tao You, Hao Jiang, Zhi-yuan Yang, Chang-yuan Yang, Ling-yun Sun. Automatic synthesis of advertising images according to a specified style[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(10): 1455-1466.
@article{title="Automatic synthesis of advertising images according to a specified style",
author="Wei-tao You, Hao Jiang, Zhi-yuan Yang, Chang-yuan Yang, Ling-yun Sun",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="21",
number="10",
pages="1455-1466",
year="2020",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1900367"
}
%0 Journal Article
%T Automatic synthesis of advertising images according to a specified style
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%A Hao Jiang
%A Zhi-yuan Yang
%A Chang-yuan Yang
%A Ling-yun Sun
%J Frontiers of Information Technology & Electronic Engineering
%V 21
%N 10
%P 1455-1466
%@ 2095-9184
%D 2020
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1900367
TY - JOUR
T1 - Automatic synthesis of advertising images according to a specified style
A1 - Wei-tao You
A1 - Hao Jiang
A1 - Zhi-yuan Yang
A1 - Chang-yuan Yang
A1 - Ling-yun Sun
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 21
IS - 10
SP - 1455
EP - 1466
%@ 2095-9184
Y1 - 2020
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1900367
Abstract: Images are widely used by companies to advertise their products and promote awareness of their brands. The automatic synthesis of advertising images is challenging because the advertising message must be clearly conveyed while complying with the style required for the product, brand, or target audience. In this study, we proposed a data-driven method to capture individual design attributes and the relationships between elements in advertising images with the aim of automatically synthesizing the input of elements into an advertising image according to a specified style. To achieve this multi-format advertisement design, we created a dataset containing 13 280 advertising images with rich annotations that encompassed the outlines and colors of the elements, in addition to the classes and goals of the advertisements. Using our probabilistic models, users guided the style of synthesized advertisements via additional constraints (e.g., context-based keywords). We applied our method to a variety of design tasks, and the results were evaluated in several perceptual studies, which showed that our method improved users’ satisfaction by 7.1% compared to designs generated by nonprofessional students, and that more users preferred the coloring results of our designs to those generated by the color harmony model and Colormind.
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