Toward Model Building for Visual Aesthetic Perception

Liu, Jianli; Lughofer, Edwin; Zeng, Xiaoyang

doi:10.1155/2017/1292801

Cited by 19 publications

(14 citation statements)

References 66 publications

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“…The present review article discusses the two most related visual aesthetics models: Chatterjee's model of the neural underpinnings of visual aesthetics and the model of aesthetic appreciation and aesthetic judgment [ 18 ]. These two models are the most suitable and related to aesthetic processing on UX design and clearly show the initial, the intermediate, and the overall aesthetic process for designing visual interaction projects.…”

Section: Methodsmentioning

confidence: 99%

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A Review of EEG and fMRI Measuring Aesthetic Processing in Visual User Experience Research

Rui

2021

Computational Intelligence and Neuroscience

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In human-computer interaction, the visual interaction of user experience (UX) and user interface (UI) plays an important role in enriching the quality of daily life. The purpose of our study analyzes the use of brain-computer interface (BCI), wearable technology, and functional magnetic resonance imaging (fMRI) to explore the aesthetic processing of visual neural response to UI and UX designs. Specifically, this review aims to understand neuroaesthetic processing knowledge, aesthetic appreciation models, and the ways in which visual brain studies can improve the quality of current and future UI and UX designs. Recent research has found that subjective evaluations of aesthetic appreciation produce different results for objective evaluations of brain research analysis. We applied SWOT analysis and examined the advantages and disadvantages of both evaluation methods. Furthermore, we conducted a traditional literature review on topics pertaining to the use of aesthetic processing knowledge in the visual interaction field in terms of art therapy, information visualization, website or mobile applications, and other interactive platforms. Our main research findings from current studies have helped and motivated researchers and designers to use convincing scientific knowledge of brain event-related potential, electroencephalography, and fMRI to understand aesthetic judgment. The key trend finds that many designers, artists, and engineers use artistic BCI technology in the visual interaction experience. Herein, the scientific methods applied in the aesthetic appreciation to human-computer interface are summarized, and the influence of the latest wearable brain technology on visual interaction design is discussed. Furthermore, current possible research entry points for aesthetics, usability, and creativity in UI and UX designs are explicated. The study results have implications for the visual user experience research domain as well as for interaction industries, which produce interactive projects to improve people’s daily lives.

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Section: Methodsmentioning

confidence: 99%

“…It entails continuous evaluation of the UX design. Figure 2 summarizes the UX components related to the aesthetic appreciation model (Model 2) [ 18 , 22 ].…”

Section: Methodsmentioning

confidence: 99%

A Review of EEG and fMRI Measuring Aesthetic Processing in Visual User Experience Research

Rui

2021

Computational Intelligence and Neuroscience

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“…Liu's research explained the principle of subjective perception in the translation of objective environmental information of landscapes. He established a quantitative assessment standard and index system and proposed a technical method for measuring and simulating landscape openness and serene ranking (Liu et al, 2017). This work plays an important role in the further construction of a multi-level weight evaluation system.…”

Section: Core Concepts Visual Perceptionmentioning

confidence: 99%

Greenway Cyclists’ Visual Perception and Landscape Imagery Assessment

Hui

Lin

et al. 2021

Front. Psychol.

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Greenway is a kind of corridors in the city that takes natural elements as the main constituent foundation and connects open spaces with functions such as leisure and recreation. The assessment of the built greenway is a review of the past construction experiences, and it is also a supplement and improvement to the future greenway planning concept system, which has important academic and application value. This study will explore how greenway design factors influenced the local cyclists’ perception of the landscape using on-site questionnaire and photo rating method. The results indicated that greenways with continuous cycling paths, high security awareness, open landscapes, and rich human activities evoke positive perceptions. Among the visual elements, natural elements such as plants and sky are more favorable than artificial elements. The research results show that the formation of greenway cyclists’ landscape imagery is affected by visual perception elements, which suggests that special consideration should be given to the laws of cyclists’ mental perception when designing greenways.

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“…It is known that any visual stimulus has a cognitive effect in the receiver and can induce emotional responses [1,2]. However, the mechanisms that underlie these processes are still largely unknown, making it difficult to find an automatic computational solution to the problem, despite the currently proposed methods [3].…”

Section: Introductionmentioning

confidence: 99%

Predicting Image Aesthetics for Intelligent Tourism Information Systems

et al. 2019

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Image perception can vary considerably between subjects, yet some sights are regarded as aesthetically pleasant more often than others due to their specific visual content, this being particularly true in tourism-related applications. We introduce the ESITUR project, oriented towards the development of ’smart tourism’ solutions aimed at improving the touristic experience. The idea is to convert conventional tourist showcases into fully interactive information points accessible from any smartphone, enriched with automatically-extracted contents from the analysis of public photos uploaded to social networks by other visitors. Our baseline, knowledge-driven system reaches a classification accuracy of 64.84 ± 4.22% telling suitable images from unsuitable ones for a tourism guide application. As an alternative we adopt a data-driven Mixture of Experts (MEX) approach, in which multiple learners specialize in partitions of the problem space. In our case, a location tag is attached to every picture providing a criterion to segment the data by, and the MEX model accordingly defined achieves an accuracy of 85.08 ± 2.23%. We conclude ours is a successful approach in environments in which some kind of data segmentation can be applied, such as touristic photographs.

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Toward Model Building for Visual Aesthetic Perception

Cited by 19 publications

References 66 publications

A Review of EEG and fMRI Measuring Aesthetic Processing in Visual User Experience Research

A Review of EEG and fMRI Measuring Aesthetic Processing in Visual User Experience Research

Greenway Cyclists’ Visual Perception and Landscape Imagery Assessment

Predicting Image Aesthetics for Intelligent Tourism Information Systems

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