Studying Three Abstract Artists Based on a Multiplex Network Knowledge Representation
Discovering the influences between paintings and artists is very important for automatic art analysis. Lately, this problem has gained more importance since research studies are looking into explanations about the origin and evolution of artistic styles, which is a related problem. This paper propos...
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2021-01-01
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Series: | Complexity |
Online Access: | http://dx.doi.org/10.1155/2021/8506571 |
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doaj-f9c740d182554b7ca33ca461fa3250d02021-04-19T00:04:22ZengHindawi-WileyComplexity1099-05262021-01-01202110.1155/2021/8506571Studying Three Abstract Artists Based on a Multiplex Network Knowledge RepresentationLuis Fernando Gutiérrez0Roberto Zarama1Juan Alejandro Valdivia2Departamento de Ingeniería IndustrialDepartamento de Ingeniería IndustrialDepartamento de FísicaDiscovering the influences between paintings and artists is very important for automatic art analysis. Lately, this problem has gained more importance since research studies are looking into explanations about the origin and evolution of artistic styles, which is a related problem. This paper proposes to build a multiplex artwork representation based on artistic formal concepts to gain more understanding about the aforementioned problem. We complement and built our approach on the previous notion of Creativity Implication Network. We used the recently proposed MultiRank algorithm to suggest possible explanations of the dynamic of some artistic styles. Our results corroborate some well-known facts about the artists analyzed and give qualitative and quantitative information that show the possibilities and strengths of the proposed framework. We plan to expand our analysis to include more abstract artworks. Ideally, we are going to be able to validate more our results and test how our methodology could be used to generate visual artifacts too.http://dx.doi.org/10.1155/2021/8506571 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Luis Fernando Gutiérrez Roberto Zarama Juan Alejandro Valdivia |
spellingShingle |
Luis Fernando Gutiérrez Roberto Zarama Juan Alejandro Valdivia Studying Three Abstract Artists Based on a Multiplex Network Knowledge Representation Complexity |
author_facet |
Luis Fernando Gutiérrez Roberto Zarama Juan Alejandro Valdivia |
author_sort |
Luis Fernando Gutiérrez |
title |
Studying Three Abstract Artists Based on a Multiplex Network Knowledge Representation |
title_short |
Studying Three Abstract Artists Based on a Multiplex Network Knowledge Representation |
title_full |
Studying Three Abstract Artists Based on a Multiplex Network Knowledge Representation |
title_fullStr |
Studying Three Abstract Artists Based on a Multiplex Network Knowledge Representation |
title_full_unstemmed |
Studying Three Abstract Artists Based on a Multiplex Network Knowledge Representation |
title_sort |
studying three abstract artists based on a multiplex network knowledge representation |
publisher |
Hindawi-Wiley |
series |
Complexity |
issn |
1099-0526 |
publishDate |
2021-01-01 |
description |
Discovering the influences between paintings and artists is very important for automatic art analysis. Lately, this problem has gained more importance since research studies are looking into explanations about the origin and evolution of artistic styles, which is a related problem. This paper proposes to build a multiplex artwork representation based on artistic formal concepts to gain more understanding about the aforementioned problem. We complement and built our approach on the previous notion of Creativity Implication Network. We used the recently proposed MultiRank algorithm to suggest possible explanations of the dynamic of some artistic styles. Our results corroborate some well-known facts about the artists analyzed and give qualitative and quantitative information that show the possibilities and strengths of the proposed framework. We plan to expand our analysis to include more abstract artworks. Ideally, we are going to be able to validate more our results and test how our methodology could be used to generate visual artifacts too. |
url |
http://dx.doi.org/10.1155/2021/8506571 |
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