Art-Style Classification Using MobileNetV2: A Deep Learning Approach

Zhengyang Wang

2025

Abstract

Painting, as a significant component of human culture, carries historical and cultural information while shaping aesthetic perceptions. However, the complexity of artistic styles often makes it challenging for the general public to comprehend them in depth. Leveraging artificial intelligence to popularize art knowledge and facilitate the recognition and understanding of artistic styles intuitively has thus become increasingly important. This study applies MobileNetV2 to develop an art style classification system that automatically identifies eight painting genres, including Abstract Art and Romanticism, showcasing their historical and cultural significance. The research is based on the WikiArt dataset, covering eight classic painting styles with approximately 3,600 images. By employing data preprocessing, transfer learning, and the MobileNetV2 model, the system achieves art-style classification, with data augmentation and hyperparameter optimization enhancing model performance. The target accuracy for the system is set at ≥65%. This study aims to provide an innovative tool for art education and aesthetic appreciation by implementing artificial intelligence techniques for the automatic classification of classical painting styles. The findings contribute to enhancing public understanding and appreciation of painting art while advancing practical applications of artificial intelligence in the art domain.

Paper Citation

in Harvard Style

Wang Z. (2025). Art-Style Classification Using MobileNetV2: A Deep Learning Approach. In Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE; ISBN 978-989-758-765-8, SciTePress, pages 16-20. DOI: 10.5220/0013677300004670

in Bibtex Style

@conference{icdse25,
author={Zhengyang Wang},
title={Art-Style Classification Using MobileNetV2: A Deep Learning Approach},
booktitle={Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE},
year={2025},
pages={16-20},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013677300004670},
isbn={978-989-758-765-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE
TI - Art-Style Classification Using MobileNetV2: A Deep Learning Approach
SN - 978-989-758-765-8
AU - Wang Z.
PY - 2025
SP - 16
EP - 20
DO - 10.5220/0013677300004670
PB - SciTePress