Evidence map›Paper›PMID 31961909›Full record

ArticlePloS one2020

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

Baixi Xing, Kejun Zhang, Lekai Zhang, Xinda Wu, Huahao Si, Hui Zhang, Kaili Zhu, Shouqian Sun

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.1field-weighted citation impact, top 60% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 3 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 2 institutions in 1 country.

Baixi XingInstitute of Industrial Design, Zhejiang University of Technology, Hangzhou, China.ORCID 0000-0001-6778-3650
Kejun ZhangCollege of Computer Science and Technology, Zhejiang University, Hangzhou, China.
Lekai ZhangInstitute of Industrial Design, Zhejiang University of Technology, Hangzhou, China.
Xinda WuCollege of Computer Science and Technology, Zhejiang University, Hangzhou, China.
Huahao SiSchool of Media and Design, Hangzhou Dianzi University, Hangzhou, China.
Hui ZhangCollege of Computer Science and Technology, Zhejiang University, Hangzhou, China.
Kaili ZhuCollege of Computer Science and Technology, Zhejiang University, Hangzhou, China.
Shouqian SunCollege of Computer Science and Technology, Zhejiang University, Hangzhou, China.
Zhejiang University of Science and Technology · CNHangzhou Dianzi University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Awards and PrizesEstheticsMachine LearningComputer SimulationDatasets as TopicEquipment DesignHumansImage Processing, Computer-Assisted

Identifiers

PMID31961909
PMCPMC6974033
OpenAlexW3000964988

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.