research-article

A study on the automatic generation of banner layouts

Authors:

Yanxue LiangAuthors Info & Claims

Volume 93, Issue C

https://doi.org/10.1016/j.compeleceng.2021.107269

Published: 01 July 2021 Publication History

Highlights

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Filling design elements to the layout to generate a banner

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Aesthetic measures to evaluate the design layout

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Traditional method to optimize the design layout based on designers’ experience

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Optimize the design layout automatically with deep reinforcement learning

Abstract

This paper addresses the automatic banner design problem as a layout generation task, where a banner is generated by filling design elements to a given layout. We initially introduce the traditional method of generating design layouts used by designers. After that, we introduce a deep reinforcement learning (DRL) based method to learn the policy of generating design layouts. Evaluation metrics are introduced to assess the quality of design layouts generated by the two proposed methods. The experiment shows that, both methods can generate a layout for a banner of given size, and the DRL based method outperforms the traditional method by generating layouts of better quality. However, results based on the DRL method are affected by reward definitions and a long training process is needed to achieve a better performance.

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References

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Cited By

Fukaya KDaylamani-Zad DAgius H(2025)Intelligent Generation of Graphical Game Assets: A Conceptual Framework and Systematic Review of the State of the ArtACM Computing Surveys10.1145/370849957:5(1-38)Online publication date: 9-Jan-2025
https://dl.acm.org/doi/10.1145/3708499
Zhang MLiu FLi BLiu ZMa WRan C(2024)CrePosterExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.123136245:COnline publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1016/j.eswa.2024.123136
Hu HZhang CLiang Y(2022)Banner layout retargeting with hierarchical reinforcement learning and variational autoencoderMultimedia Tools and Applications10.1007/s11042-022-13325-w81:24(34417-34438)Online publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1007/s11042-022-13325-w

Index Terms

A study on the automatic generation of banner layouts
1. Computing methodologies
  1. Machine learning
2. Human-centered computing

Index terms have been assigned to the content through auto-classification.

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Information

Published In

cover image Computers and Electrical Engineering

Computers and Electrical Engineering Volume 93, Issue C

Jul 2021

1045 pages

ISSN:0045-7906

Issue’s Table of Contents

Copyright © 2021.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 01 July 2021

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Cited By

Fukaya KDaylamani-Zad DAgius H(2025)Intelligent Generation of Graphical Game Assets: A Conceptual Framework and Systematic Review of the State of the ArtACM Computing Surveys10.1145/370849957:5(1-38)Online publication date: 9-Jan-2025
https://dl.acm.org/doi/10.1145/3708499
Zhang MLiu FLi BLiu ZMa WRan C(2024)CrePosterExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.123136245:COnline publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1016/j.eswa.2024.123136
Hu HZhang CLiang Y(2022)Banner layout retargeting with hierarchical reinforcement learning and variational autoencoderMultimedia Tools and Applications10.1007/s11042-022-13325-w81:24(34417-34438)Online publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1007/s11042-022-13325-w

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