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.

翻译：本文针对波斯细密画这一丰富文化遗产在计算绘画分析领域的研究空白，提出了一种基于卷积神经网络（CNN）的创新分类方法。该方法可对赫拉特、大不里士第一、设拉子第一、大不里士第二及卡扎尔五大流派的波斯细密画进行自动分类，平均准确率超过91%。研究构建了精心标注的数据集以捕捉各流派的独特艺术特征，采用基于图像块的CNN架构对图像片段进行独立分类后融合结果，从而提升分类精度。本研究通过详细阐述数据集构建、CNN架构设计、训练与验证流程，为数字艺术分析领域作出重要贡献。该工作展现了自动化艺术分析在机器学习、艺术史与数字人文交叉领域的巨大潜力，为波斯文化遗产的保护与理解提供了新的技术途径。