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CLC number: TP391

On-line Access: 2020-10-14

Received: 2019-07-22

Revision Accepted: 2019-12-08

Crosschecked: 2020-06-19

Cited: 0

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Citations:  Bibtex RefMan EndNote GB/T7714


Wei-tao You


Hao Jiang


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Frontiers of Information Technology & Electronic Engineering  2020 Vol.21 No.10 P.1455-1466


Automatic synthesis of advertising images according to a specified style

Author(s):  Wei-tao You, Hao Jiang, Zhi-yuan Yang, Chang-yuan Yang, Ling-yun Sun

Affiliation(s):  Key Laboratory of Design Intelligence and Digital Creativity of Zhejiang Province, Hangzhou 310027, China ; more

Corresponding email(s):   weitao_you@zju.edu.cn, jiang_hao@zju.edu.cn, youngs@zju.edu.cn, changyuan.yangcy@alibaba-inc.com

Key Words:  Image dataset, Data-driven method, Automatic advertisement synthesis

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.

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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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