And the nominees are: Using design-awards datasets to build computational aesthetic evaluation model


Autoři: Baixi Xing aff001;  Kejun Zhang aff002;  Lekai Zhang aff001;  Xinda Wu aff002;  Huahao Si aff003;  Hui Zhang aff002;  Kaili Zhu aff002;  Shouqian Sun aff002
Působiště autorů: Institute of Industrial Design, Zhejiang University of Technology, Hangzhou, China aff001;  College of Computer Science and Technology, Zhejiang University, Hangzhou, China aff002;  School of Media and Design, Hangzhou Dianzi University, Hangzhou, China aff003
Vyšlo v časopise: PLoS ONE 15(1)
Kategorie: Research Article
doi: 10.1371/journal.pone.0227754

Souhrn

Aesthetic perception is a human instinct that is responsive to multimedia stimuli. Giving computers the ability to assess human sensory and perceptual experience of aesthetics is a well-recognized need for the intelligent design industry and multimedia intelligence study. In this work, we constructed a novel database for the aesthetic evaluation of design, using 2,918 images collected from the archives of two major design awards, and we also present a method of aesthetic evaluation that uses machine learning algorithms. Reviewers’ ratings of the design works are set as the ground-truth annotations for the dataset. Furthermore, multiple image features are extracted and fused. The experimental results demonstrate the validity of the proposed approach. Primary screening using aesthetic computing can be an intelligent assistant for various design evaluations and can reduce misjudgment in art and design review due to visual aesthetic fatigue after a long period of viewing. The study of computational aesthetic evaluation can provide positive effect on the efficiency of design review, and it is of great significance to aesthetic recognition exploration and applications development.

Klíčová slova:

Algorithms – Artificial intelligence – Gene pool – Imaging techniques – Machine learning – Machine learning algorithms – Neural networks – Support vector machines


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Článek vyšel v časopise

PLOS One


2020 Číslo 1