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Computational Model for Predicting User Aesthetic Preference for GUI using DCNNs

Xing, Baixi; Si, Huahao; Chen, Junbin; Ye, Minchao; Shi, Lei

Computational Model for Predicting User Aesthetic Preference for GUI using DCNNs Thumbnail


Authors

Baixi Xing

Huahao Si

Junbin Chen

Minchao Ye

Lei Shi



Abstract

Visual aesthetics is vital in determining the usability of the graphical user interface (GUI). It can strengthen the competitiveness of interactive online applications. Human aesthetic preferences for GUI are implicit and linked to various aspects of perception. In this study, an aesthetic GUI image database was constructed with 38,423 design works collected from Huaban.com, a popular social network website for art and design sharing, collection, and exhibition in China. The numbers of user collection and likes of each design work were used as the annotation to represent user preference levels. Deep convolutional neural networks were applied to evaluate the aesthetic preferences of GUIs, based on a large dataset of user interface design images with the ground-truth annotations. The experimental result indicated the feasibility of the proposed method, with a mean squared error (MSE) of 0.0222 for user collection prediction and an MSE of 0.0644 for user likes prediction in the best model performance of Squeeze-and-Excitation-VGG19 networks (SE-VGG19). This study aims to build a large aesthetic image database, and to explore a practical and objective evaluation model of GUI aesthetics.

Citation

Xing, B., Si, H., Chen, J., Ye, M., & Shi, L. (2021). Computational Model for Predicting User Aesthetic Preference for GUI using DCNNs. CCF Transactions on Pervasive Computing and Interaction, 3, 147-169. https://doi.org/10.1007/s42486-021-00064-4

Journal Article Type Article
Acceptance Date Mar 29, 2021
Online Publication Date Apr 21, 2021
Publication Date 2021-06
Deposit Date May 5, 2021
Publicly Available Date Mar 29, 2024
Journal CCF Transactions on Pervasive Computing and Interaction
Print ISSN 2524-521X
Electronic ISSN 2524-5228
Publisher Springer
Peer Reviewed Peer Reviewed
Volume 3
Pages 147-169
DOI https://doi.org/10.1007/s42486-021-00064-4

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Copyright Statement
This is a post-peer-review, pre-copyedit version of a journal article published in CCF Transactions on Pervasive Computing and Interaction. The final authenticated version is available online at: https://doi.org/10.1007/s42486-021-00064-4





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