CNN-Based Classification of Persian Miniature Paintings from Five Renowned Schools

TL;DR

This paper presents a CNN-based method for classifying Persian miniature paintings from five different schools, achieving over 91% accuracy and advancing digital art analysis of this cultural heritage.

Contribution

It introduces a novel patch-based CNN approach and a curated dataset for classifying Persian miniatures, bridging machine learning and art history.

Findings

Achieved over 91% classification accuracy.

Developed a patch-based CNN architecture.

Provided a curated dataset for Persian miniatures.

Abstract

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.