Indian folk paintings have a rich mosaic of symbols, colors, textures, and stories making them an invaluable repository of cultural legacy. The paper presents a novel approach to classifying these paintings into distinct art forms and tagging them with their unique salient features. A custom dataset named FolkTalent, comprising 2279 digital images of paintings across 12 different forms, has been prepared using websites that are direct outlets of Indian folk paintings. Tags covering a wide range of attributes like color, theme, artistic style, and patterns are generated using GPT4, and verified by an expert for each painting. Classification is performed employing the RandomForest ensemble technique on fine-tuned Convolutional Neural Network (CNN) models to classify Indian folk paintings, achieving an accuracy of 91.83%. Tagging is accomplished via the prominent fine-tuned CNN-based backbones with a custom classifier attached to its top to perform multi-label image classification. The generated tags offer a deeper insight into the painting, enabling an enhanced search experience based on theme and visual attributes. The proposed hybrid model sets a new benchmark in folk painting classification and tagging, significantly contributing to cataloging India's folk-art heritage.

翻译：印度民间绘画蕴含着丰富的符号、色彩、纹理与故事，是文化遗产的宝贵宝库。本文提出了一种新颖方法，用于将这些绘画分类为不同的艺术形式，并为其标注独特的显著特征。我们构建了一个名为FolkTalent的自定义数据集，包含来自12种不同形式的2279幅数字图像，这些图像取自直接销售印度民间绘画的网站。利用GPT4为每幅画生成涵盖色彩、主题、艺术风格和图案等多维属性的标签，并由专家逐一验证。分类方面，采用随机森林集成技术基于微调的卷积神经网络模型对印度民间绘画进行分类，准确率达91.83%。标注则通过基于微调CNN的骨干网络实现，在其顶部附加自定义分类器以执行多标签图像分类。生成的标签可为绘画提供更深入的见解，实现基于主题和视觉属性的增强搜索体验。所提出的混合模型在民间绘画分类与标注领域树立了新基准，为记录印度民间艺术遗产作出了重要贡献。