A language to analyze, describe, and explore collections of visual art.
Collections of visual art
Digital humanities
Personalized digital libraries
Semantics
Visual computing
Journal
Visual computing for industry, biomedicine, and art
ISSN: 2524-4442
Titre abrégé: Vis Comput Ind Biomed Art
Pays: Germany
ID NLM: 101759975
Informations de publication
Date de publication:
01 Mar 2021
01 Mar 2021
Historique:
received:
13
07
2020
accepted:
08
02
2021
entrez:
1
3
2021
pubmed:
2
3
2021
medline:
2
3
2021
Statut:
epublish
Résumé
A vast quantity of art in existence today is inaccessible to individuals. If people want to know the different types of art that exist, how individual works are connected, and how works of art are interpreted and discussed in the context of other works, they must utilize means other than simply viewing the art. Therefore, this paper proposes a language to analyze, describe, and explore collections of visual art (LadeCA). LadeCA combines human interpretation and automatic analyses of images, allowing users to assess collections of visual art without viewing every image in them. This paper focuses on the lexical base of LadeCA. It also outlines how collections of visual art can be analyzed, described, and explored using a LadeCA vocabulary. Additionally, the relationship between LadeCA and indexing systems, such as ICONCLASS or AAT, is demonstrated, and ways in which LadeCA and indexing systems can complement each other are highlighted. Video abstract.
Identifiants
pubmed: 33646448
doi: 10.1186/s42492-021-00071-3
pii: 10.1186/s42492-021-00071-3
pmc: PMC7921272
doi:
Types de publication
Journal Article
Langues
eng
Pagination
5Références
IEEE Trans Vis Comput Graph. 2017 Jan;23(1):801-810
pubmed: 27875194
Vis Comput Ind Biomed Art. 2020 Feb 5;3(1):3
pubmed: 32240430
IEEE Trans Vis Comput Graph. 2018 Jan;24(1):413-423
pubmed: 28866530
IEEE Trans Neural Netw. 1997;8(1):98-113
pubmed: 18255614
IEEE Trans Vis Comput Graph. 2020 Oct;26(10):3063-3076
pubmed: 30946669
IEEE Comput Graph Appl. 2018 Jan;38(1):91-108
pubmed: 28991735