A style classification algorithm for ink painting based on deep network feature aggregation

Xuezeng Zhang

doi:10.1117/12.3026604

27 March 2024 A style classification algorithm for ink painting based on deep network feature aggregation

Xuezeng Zhang

Proceedings Volume 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023); 131054H (2024) https://doi.org/10.1117/12.3026604
Event: 3rd International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 2023, Qingdao, China

Abstract

This study proposes a style classification algorithm for ink painting works based on deep network feature aggregation. Introduce the deep classification regression tree feature aggregation method, use Bayes discriminant criterion, obtain probability density function, and extract the style features of ink painting works. Based on this, a sample dataset for ink painting style classification is constructed, and the Apriori association rule is used to use the corresponding ink painting style category as the final output result of the classifier to complete the classification. The experimental results show that the research method has good performance in extracting style features of ink painting, with short time consumption, low classification error, and high fault tolerance.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xuezeng Zhang "A style classification algorithm for ink painting based on deep network feature aggregation", Proc. SPIE 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 131054H (27 March 2024); https://doi.org/10.1117/12.3026604

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KEYWORDS

Feature extraction

Education and training

Image classification

Tolerancing

Data modeling

Deep learning

Mining

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