<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>With the development of the Internet, many art works are uploaded for people to enjoy. To make it more convenient for viewers to understand and analyze the content of art works. In this paper, we use the GNN graph neural network analysis model to extract the art style, creation period, color expression and other features of the works through the work feature saliency attention module, and classify them using the similarity metric. At the same time, we put forward the GNNMMVisRe model to explore the works from the visual multimodality. The analysis results concluded that the Impressionist style of art works had its heyday in the 18th-19th centuries, when the number of works reached 11,674. The Baroque classicism style reached its heyday in the mid-18th century with 5,921 pieces. In terms of color use, low saturation and low luminance color palettes were selected, with average values of 81.3% and 83.4%, respectively. Using the GNN model to categorize, analyze and study the art works improves the understanding and appreciation of the works. It is also significant for the further development of art.</p>
</abstract>

With the development of the Internet, many art works are uploaded for people to enjoy. To make it more convenient for viewers to understand and analyze the content of art works. In this paper, we use the GNN graph neural network analysis model to extract the art style, creation period, color expression and other features of the works through the work feature saliency attention module, and classify them using the similarity metric. At the same time, we put forward the GNNMMVisRe model to explore the works from the visual multimodality. The analysis results concluded that the Impressionist style of art works had its heyday in the 18th-19th centuries, when the number of works reached 11,674. The Baroque classicism style reached its heyday in the mid-18th century with 5,921 pieces. In terms of color use, low saturation and low luminance color palettes were selected, with average values of 81.3% and 83.4%, respectively. Using the GNN model to categorize, analyze and study the art works improves the understanding and appreciation of the works. It is also significant for the further development of art.

A study on the analysis and understanding of art works based on graph neural networks

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

With the development of the Internet, many art works are uploaded for people to enjoy. To make it more convenient for viewers to understand and analyze the...

A study on the analysis and understanding of art works based on graph neural networks

Online veröffentlicht: 26. Feb. 2024

Seitenbereich: -

Eingereicht: 04. Jan. 2024

Akzeptiert: 09. Jan. 2024

DOI: https://doi.org/10.2478/amns-2024-0367

Schlüsselwörter
Graph neural network, Attention module, GNNMMVisRe, Fine art works

© 2024 Hua Li, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

A study on the analysis and understanding of art works based on graph neural networks

Online veröffentlicht: 26. Feb. 2024

Seitenbereich: -

Eingereicht: 04. Jan. 2024

Akzeptiert: 09. Jan. 2024

DOI: https://doi.org/10.2478/amns-2024-0367

SchlüsselwörterGraph neural network, Attention module, GNNMMVisRe, Fine art works

© 2024 Hua Li, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Graph neural network, Attention module, GNNMMVisRe, Fine art works