A Model to Compute Webpage Aesthetics Quality Based on Wireframe Geometry

Maity, Ranjan; Bhattacharya, Samit

doi:10.1007/978-3-319-67687-6_7

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…Researchers have mainly focused on two hand-crafted visual features: colorfulness [6,29] and visual complexity [27,51,56]. However, there are more visual features that may impact the user's appealing perception, such as symmetry [50], compositional elements [2], wireframe geometry [26], balance [20], harmony [30], novelty [12], and typography [23]. Quantifying all these features is hard because there is no measurement consensus; see e.g.…”

Section: Related Workmentioning

confidence: 99%

Modeling how different user groups perceive webpage aesthetics

Leiva

Shiripour²,

Oulasvirta³

2022

Univ Access Inf Soc

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Aesthetics is a central consideration in user interface design. It is known to affect end-user behavior and perception, in particular the first impression of graphical user interfaces. However, what users perceive as pleasant or good design is highly subjective. We contribute a computational model that estimates the visual appeal of a given webpage for several common cohorts, or user groups, including gender, age, and education level. Our model, a convolutional neural network trained on 418 webpage screenshots having 771k aesthetic scores (in a 1-9 Likert scale) from 32k users, achieves high accuracy and is always less than 1 point off from ground-truth ratings. Designers can use our model to anticipate how people would rate their webpage, offer personalized designs according to the visual preferences of their users, and support rapid evaluations of webpage design prototypes for specific cohorts.

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Section: Related Workmentioning

confidence: 99%

Modeling how different user groups perceive webpage aesthetics

Leiva

Shiripour²,

Oulasvirta³

2022

Univ Access Inf Soc

View full text Add to dashboard Cite

show abstract

“…For example, Kang [2] introduced electric field mechanics into the field of aesthetic evaluation and proposed a color aesthetic evaluation method based on the combination of form and color. Ranjan [3] proposed a computational model for predicting web aesthetics based on the linear kernels of support vector machines. Kobayashi [4], based on the analysis of the relationship between customer sensibility and aesthetic elements, proposed a method to support design processes with aesthetics and explored an optimal aesthetic design approach with a genetic algorithm.…”

Section: A Aesthetic Evaluationmentioning

confidence: 99%

Evaluation and Design Method for Product Form Aesthetics Based on Deep Learning

et al. 2021

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Currently, evaluations of products from aesthetics are mostly carried out with knowledge expressions of aesthetic features as tools, achieving remarkable results. However, obtaining a large aesthetic feature vocabulary is a challenge because of the experience of researchers and the comprehension abilities of subjects. In addition, due to manual feature extraction, the sample sizes of experimental dataset are generally small, leading to results with poor generalization. To address this problem, a method of aesthetic evaluation and form design for products based on deep learning was proposed. First, a crawler tool was used to collect the front images of cars with corresponding appearance ratings, and a dataset was constructed with users' intuitive and simple ratings as the labels. A deep convolutional neural network (CNN) was designed, and a grading threshold was used as the classification basis. During the process of training the network, batch normalization and other methods were used to optimize the network, and good classification effects were achieved. Based on the above work, an adversarial neural network was used for the aesthetic design of a product form, a shape sketch of an automobile front face was generated, the proposed evaluation model was used to evaluate it, and the result obtained was excellent. These results show that the method used in this study can correctly evaluate product form aesthetics and then generate a design scheme with a high aesthetic level, thereby providing powerful technical support for the intelligent design of product forms.

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“…Another aesthetic computing study of onscreen images by R. Maity et al achieved an MSE of 0.03 in rating prediction based on a non-linear regression model with an SVM classifier [4]. Later in 2017, they investigated a feature set of 13 independent webpage characters using SVM modeling to predict the aesthetic score with an accuracy of 90% [17].…”

Section: Aesthetic Modeling For Websitesmentioning

confidence: 99%

Computational model for predicting user aesthetic preference for GUI using DCNNs

Xing

Chen

et al. 2021

CCF Trans. Pervasive Comp. Interact.

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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.

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A Model to Compute Webpage Aesthetics Quality Based on Wireframe Geometry

Cited by 4 publications

References 11 publications

Modeling how different user groups perceive webpage aesthetics

Modeling how different user groups perceive webpage aesthetics

Evaluation and Design Method for Product Form Aesthetics Based on Deep Learning

Computational model for predicting user aesthetic preference for GUI using DCNNs

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