The Brain-Machine-Ratio Model for Designer and AI Collaboration

Li, Fan; Bao, Yongxing; Gong, Shuyu; Yan, Sida; Wang, Harry Jiannan

doi:10.1109/mipr51284.2021.00058

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2023

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“…Computer vision provides a convenient aid in design process [1], which can provide the method of image aesthetic assessment. Aesthetics is a criterion in design process, integrating aesthetics into machine learning to realize the auxiliary effect that meets the subjective aesthetic needs of human beings.…”

Section: Introductionmentioning

confidence: 99%

Aesthetics Assessment of Automobile Headlights Based on Image Aesthetic Network

Jiang

Liu

Pan

et al. 2023

Preprint

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Automatically learned aesthetic assessment for images can provide auxiliary value for the fields of art and design. In this study, we explore the aesthetics assessment methods on the visual esthetics of automobile headlamp images, and compare the feasibility of the calculation method of artificial aesthetic degree evaluation. We take the Image Aesthetics Assessment Network using Graph Attention (AAGN) for the automobile headlamps dataset calibration. To enable testing the effectiveness of AAGN, we apply the method of artificial aesthetics degree calculation and entropy weight, as well as assessment methods based on deep Convolutional Neural Network. The results show that the feasibility of the image aesthetic assessment method based on graph attention network is more suitable for users’ subjective aesthetic needs than artificial aesthetics degree calculation.

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Section: Introductionmentioning

confidence: 99%