用CNN识别波斯细密画五大学派，平均准确率超91%。
CNN-Based Classification of Persian Miniature Paintings from Five Renowned Schools
- 分块训练CNN，独立分类图像片段后融合结果
- 在自建数据集上实现超过91%的平均分类准确率
- 适合艺术史与数字人文研究者参考
本文填补了计算艺术分析中针对波斯细密画这一丰富文化遗产的研究空白。提出一种基于卷积神经网络（CNN）的新方法，用于识别来自五个著名学派——赫拉特、早期塔布里兹、早期设拉子、晚期塔布里兹和卡扎尔——的波斯细密画作品。该方法在精心构建的数据集上实现了超过91%的平均准确率。采用分块式CNN策略，对图像局部区域独立分类后再融合结果，显著提升分类精度。本研究为数字艺术分析提供了重要贡献，深入揭示了数据集构建、CNN架构设计、训练与验证流程等关键环节。研究成果展示了自动化艺术分析的潜力，推动机器学习、艺术史与数字人文学科的交叉融合，有助于波斯文化遗产的保护与理解。
原文摘要 · Abstract (English)
This article addresses the gap in computational painting analysis focused on Persian miniature painting, a rich cultural and artistic heritage. It introduces a novel approach using Convolutional Neural Networks (CNN) to classify Persian miniatures from five schools: Herat, Tabriz-e Avval, Shiraz-e Avval, Tabriz-e Dovvom, and Qajar. The method achieves an average accuracy of over 91%. A meticulously curated dataset captures the distinct features of each school, with a patch-based CNN approach classifying image segments independently before merging results for enhanced accuracy. This research contributes significantly to digital art analysis, providing detailed insights into the dataset, CNN architecture, training, and validation processes. It highlights the potential for future advancements in automated art analysis, bridging machine learning, art history, and digital humanities, thereby aiding the preservation and understanding of Persian cultural heritage.
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