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

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 constructe...

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Detalles Bibliográficos
Autores principales: Xing, Baixi, Zhang, Kejun, Zhang, Lekai, Wu, Xinda, Si, Huahao, Zhang, Hui, Zhu, Kaili, Sun, Shouqian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6974033/
https://www.ncbi.nlm.nih.gov/pubmed/31961909
http://dx.doi.org/10.1371/journal.pone.0227754
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author Xing, Baixi
Zhang, Kejun
Zhang, Lekai
Wu, Xinda
Si, Huahao
Zhang, Hui
Zhu, Kaili
Sun, Shouqian
author_facet Xing, Baixi
Zhang, Kejun
Zhang, Lekai
Wu, Xinda
Si, Huahao
Zhang, Hui
Zhu, Kaili
Sun, Shouqian
author_sort Xing, Baixi
collection PubMed
description 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.
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spelling pubmed-69740332020-02-04 And the nominees are: Using design-awards datasets to build computational aesthetic evaluation model Xing, Baixi Zhang, Kejun Zhang, Lekai Wu, Xinda Si, Huahao Zhang, Hui Zhu, Kaili Sun, Shouqian PLoS One Research Article 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. Public Library of Science 2020-01-21 /pmc/articles/PMC6974033/ /pubmed/31961909 http://dx.doi.org/10.1371/journal.pone.0227754 Text en © 2020 Xing et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Xing, Baixi
Zhang, Kejun
Zhang, Lekai
Wu, Xinda
Si, Huahao
Zhang, Hui
Zhu, Kaili
Sun, Shouqian
And the nominees are: Using design-awards datasets to build computational aesthetic evaluation model
title And the nominees are: Using design-awards datasets to build computational aesthetic evaluation model
title_full And the nominees are: Using design-awards datasets to build computational aesthetic evaluation model
title_fullStr And the nominees are: Using design-awards datasets to build computational aesthetic evaluation model
title_full_unstemmed And the nominees are: Using design-awards datasets to build computational aesthetic evaluation model
title_short And the nominees are: Using design-awards datasets to build computational aesthetic evaluation model
title_sort and the nominees are: using design-awards datasets to build computational aesthetic evaluation model
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6974033/
https://www.ncbi.nlm.nih.gov/pubmed/31961909
http://dx.doi.org/10.1371/journal.pone.0227754
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