Full Text:   <1498>

Summary:  <1006>

CLC number: TP391

On-line Access: 2020-10-14

Received: 2019-07-22

Revision Accepted: 2019-12-08

Crosschecked: 2020-06-19

Cited: 0

Clicked: 3010

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Wei-tao You

https://orcid.org/0000-0002-9625-5547

Hao Jiang

https://orcid.org/0000-0002-3530-5133

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

http://doi.org/10.1631/FITEE.1900367


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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publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1900367"
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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.

针对特定风格的平面广告图像自动生成

尤伟涛1,江浩2,杨智渊1,杨昌源3,孙凌云1
1浙江省设计智能与数字创意重点实验室,中国杭州市,310027
2浙江大学国际设计研究院,中国杭州市,310058
3阿里巴巴国际用户体验事业部,中国杭州市,311121

摘要:公司常用平面广告图像宣传产品,提高品牌知名度。设计平面广告图像不仅需要清晰地传达产品信息,还需考虑广告的目标产品、产品品牌和目标用户等内容,因此自动平面广告生成具有挑战性。本文提出数据驱动的方法捕获平面广告图像中设计特征与各个元素之间的特征关系,从而根据特定的风格,将输入的元素自动合成平面广告图像。为实现多样式的平面广告生成,构建了包含13280张平面广告图像的数据集,标签涵盖图像中产品类别、元素位置、颜色等内容。利用本文的概率模型,用户通过附加的约束(例如,基于上下文关键字)引导合成广告的风格。将本文方法用于大量设计任务,并针对生成结果进行用户感知和评价实验。结果表明,本文方法生成结果的用户满意度比非专业学生的设计结果提高了7.1%,生成的广告配色也比由色彩和谐模型与Colormind得到的结果获得更多用户好感。

关键词:图像数据集;数据驱动方法;自动平面广告生成

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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