FolkTalent: Enhancing Classification and Tagging of Indian Folk Paintings

TL;DR

This paper introduces FolkTalent, a new dataset and hybrid deep learning approach for classifying and tagging Indian folk paintings, achieving high accuracy and enriching cultural heritage cataloging.

Contribution

It presents a novel dataset, FolkTalent, and a hybrid CNN-based model for accurate classification and multi-label tagging of Indian folk paintings, enhancing cultural heritage analysis.

Findings

Achieved 91.83% classification accuracy.

Developed a comprehensive dataset with expert-verified tags.

Set a new benchmark in folk painting classification and tagging.

Abstract

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.