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<p>Interpreting the geometric structure of artworks enhances our intuitive grasp of their deeper meanings. This study employs a Capsule network model, incorporating a dynamic routing algorithm to correlate high and low-level geometric structural features of artworks. Additionally, an attention mechanism is introduced, forming a spatial attention capsule to capture the spatial context of the artwork’s geometric structure. To obtain images, a fixed-focus camera is utilized, followed by median filtering for image preprocessing and threshold segmentation using the maximum inter-class variance method to optimize recognition accuracy. The efficacy of the geometric structure recognition model, grounded in the Capsule network, is confirmed using a dataset of collected artwork images. The model achieves stability after 380 epochs, exhibiting an impressive accuracy of approximately 99.7% and a minimal loss of 0.025. Removing the attention mechanism results in a 4.06 percentage point decrease in model accuracy, whereas incorporating a dynamic routing algorithm boosts efficiency by 7.36%. Thus, the Capsule model proves highly effective in precisely recognizing and interpreting the geometric structures of artworks.</p>
</abstract>

Interpreting the geometric structure of artworks enhances our intuitive grasp of their deeper meanings. This study employs a Capsule network model, incorporating a dynamic routing algorithm to correlate high and low-level geometric structural features of artworks. Additionally, an attention mechanism is introduced, forming a spatial attention capsule to capture the spatial context of the artwork’s geometric structure. To obtain images, a fixed-focus camera is utilized, followed by median filtering for image preprocessing and threshold segmentation using the maximum inter-class variance method to optimize recognition accuracy. The efficacy of the geometric structure recognition model, grounded in the Capsule network, is confirmed using a dataset of collected artwork images. The model achieves stability after 380 epochs, exhibiting an impressive accuracy of approximately 99.7% and a minimal loss of 0.025. Removing the attention mechanism results in a 4.06 percentage point decrease in model accuracy, whereas incorporating a dynamic routing algorithm boosts efficiency by 7.36%. Thus, the Capsule model proves highly effective in precisely recognizing and interpreting the geometric structures of artworks.

Interpreting Geometric Constructions in Artworks through Capsule Network Modeling

Applied Mathematics and Nonlinear Sciences

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

Interpreting the geometric structure of artworks enhances our intuitive grasp of their deeper meanings. This study employs a Capsule network model,...

Interpreting Geometric Constructions in Artworks through Capsule Network Modeling

Online veröffentlicht: 05. Aug. 2024

Seitenbereich: -

Eingereicht: 03. Apr. 2024

Akzeptiert: 30. Juni 2024

DOI: https://doi.org/10.2478/amns-2024-1938

Schlüsselwörter
Capsule network model, Attention mechanism, Dynamic routing algorithm, Median filter, Artwork

© 2024 Xi Zhou, published by Sciendo

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

Interpreting Geometric Constructions in Artworks through Capsule Network Modeling

Online veröffentlicht: 05. Aug. 2024

Seitenbereich: -

Eingereicht: 03. Apr. 2024

Akzeptiert: 30. Juni 2024

DOI: https://doi.org/10.2478/amns-2024-1938

SchlüsselwörterCapsule network model, Attention mechanism, Dynamic routing algorithm, Median filter, Artwork

© 2024 Xi Zhou, published by Sciendo

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

Schlüsselwörter
Capsule network model, Attention mechanism, Dynamic routing algorithm, Median filter, Artwork