EnsArtNet: Ensemble neural network architecture for identifying art styles from paintings

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F26%3A0196477" target="_blank" >RIV/00216305:26220/26:0196477 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S1296207425000056" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1296207425000056</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.culher.2025.01.005" target="_blank" >10.1016/j.culher.2025.01.005</a>

Jazyk výsledku

angličtina

Název v původním jazyce

EnsArtNet: Ensemble neural network architecture for identifying art styles from paintings

Popis výsledku v původním jazyce

The digitization of paintings offers many benefits and opportunities for artists, collectors, and the public. It opens possibilities for researchers to investigate new hidden patterns that were not obvious to experts before. This work aims to develop a methodology that can identify and compare painting styles from various famous painters, such as Vincent van Gogh, Pablo Picasso, Claude Monet, and others, using an ensemble convolutional neural network (CNN). Our approach, named EnsArtNet, can distinguish between the styles of the artists' paintings with high accuracy and objectively measure the similarity with the other artists' styles. The proposed model was compared to several other state-of-the-art neural network architectures, and we show that EnsArtNet performs better than the compared one. Our model gives promising accuracy on two large-scale datasets: 84.93% on the WikiArt dataset and 86.65% on the Best Artworks of All Time dataset, which is better by more than 6% compared to other evaluated architectures. In this work, we also showed that a complex neural network architecture is efficient in this field of research, and an explanation using the GradCAM method supported it. Our methodology can help art researchers and enthusiasts analyze paintings' stylistic features and similarities and appreciate the creativity and diversity of visual arts. (c) 2025 Elsevier Masson SAS. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

Název v anglickém jazyce

EnsArtNet: Ensemble neural network architecture for identifying art styles from paintings

Popis výsledku anglicky

The digitization of paintings offers many benefits and opportunities for artists, collectors, and the public. It opens possibilities for researchers to investigate new hidden patterns that were not obvious to experts before. This work aims to develop a methodology that can identify and compare painting styles from various famous painters, such as Vincent van Gogh, Pablo Picasso, Claude Monet, and others, using an ensemble convolutional neural network (CNN). Our approach, named EnsArtNet, can distinguish between the styles of the artists' paintings with high accuracy and objectively measure the similarity with the other artists' styles. The proposed model was compared to several other state-of-the-art neural network architectures, and we show that EnsArtNet performs better than the compared one. Our model gives promising accuracy on two large-scale datasets: 84.93% on the WikiArt dataset and 86.65% on the Best Artworks of All Time dataset, which is better by more than 6% compared to other evaluated architectures. In this work, we also showed that a complex neural network architecture is efficient in this field of research, and an explanation using the GradCAM method supported it. Our methodology can help art researchers and enthusiasts analyze paintings' stylistic features and similarities and appreciate the creativity and diversity of visual arts. (c) 2025 Elsevier Masson SAS. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20203 - Telecommunications

Projekt

<a href="/cs/project/VK01010107" target="_blank" >VK01010107: Aplikace umělé inteligence pro forenzní identifikaci stop zeminových fází</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

JOURNAL OF CULTURAL HERITAGE

ISSN

1296-2074

e-ISSN

1778-3674

Svazek periodika

72

Číslo periodika v rámci svazku

2025

Stát vydavatele periodika

FR - Francouzská republika

Počet stran výsledku

10

Strana od-do

71-80

Kód UT WoS článku

001419521400001

EID výsledku v databázi Scopus

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