NEURAL NETWORKS FOR CLASSIFYING INDIAN FOLK MOTIFS
DOI:
https://doi.org/10.29121/shodhkosh.v6.i3s.2025.6758Keywords:
Indian Folk Art, Neural Networks, Cultural Heritage, Motif Classification, Grad-CAM, Transfer Learning, Digital PreservationAbstract [English]
In this paper, I have outlined a hybrid neural network model of automated classification of Indian folk motifs within the different regional traditions, such as Madhubani, Warli, Kalamkari, and Pattachitra. Introduced was a culturally authentic curated collection of 5000 high-resolution images created by ethical digitization and by human experts. The suggested model uses a Convolutional Block Attention Module (CBAM) in addition to a ResNet-50 backbone to promote the discrimination of features in space and channels. The experimental findings show better performance in comparison with the baseline CNN and VGG architectures, with the overall accuracy of 94.6, macro F1-score of 94.0 and Cohen 6. Grad-CAM visualizations show that the activations of the model are consistent with motif-specific areas of art, which verify the cultural interpretability. The framework helps to explain explainable cultural AI because it associates computational properties with heritage aesthetics, allowing them to be used in digital museums, education platforms and art restoration systems. The research provides a methodological basis of combining deep learning with the preservation of cultural heritage with a focus on transparency, reproducibility, and cross-disciplinary applicability.
References
Ajorloo, S., Jamarani, A., Kashfi, M., Kashani, M. H., and Najafizadeh, A. (2024). A Systematic Review of Machine Learning Methods in Software Testing. Applied Soft Computing, 162, Article 111805. https://doi.org/10.1016/j.asoc.2024.111805 DOI: https://doi.org/10.1016/j.asoc.2024.111805
Alzubaidi, M., et al. (2023). Large-Scale Annotation Dataset for Fetal Head Biometry in Ultrasound Images. Data in Brief, 51, Article 109708. https://doi.org/10.1016/j.dib.2023.109708 DOI: https://doi.org/10.1016/j.dib.2023.109708
Cao, D., Chen, Z., and Gao, L. (2020). An Improved Object Detection Algorithm Based on Multi-Scaled and Deformable Convolutional Neural Networks. Human-Centric Computing and Information Sciences, 10(1), 1–22. https://doi.org/10.1186/s13673-020-00219-9 DOI: https://doi.org/10.1186/s13673-020-00219-9
Dobbs, T., and Ras, Z. W. (2022). On Art Authentication and the Rijksmuseum Challenge: A Residual Neural Network Approach. Expert Systems with Applications, 200, Article 116933. https://doi.org/10.1016/j.eswa.2022.116933 DOI: https://doi.org/10.1016/j.eswa.2022.116933
Duan, C., Yin, P., Zhi, Y., and Li, X. (2019). Image Classification of Fashion-MNIST Dataset Based on VGG Network. In Proceedings of the 2nd International Conference on Information Science and Electronic Technology (ISET) (xx–xx). Taiyuan, China.
Fu, Y., Wang, W., Zhu, L., Ye, X., and Yue, H. (2024). Weakly Supervised Semantic Segmentation Based on Superpixel Affinity. Journal of Visual Communication and Image Representation, 101, Article 104168. https://doi.org/10.1016/j.jvcir.2024.104168 DOI: https://doi.org/10.1016/j.jvcir.2024.104168
Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., Kern, R., Picus, M., Hoyer, S., van Kerkwijk, M. H., Brett, M., Haldane, A., Del Río, J. F., Wiebe, M., Peterson, P., … Oliphant, T. E. (2020). Array Programming with NumPy. Nature, 585(7825), 357–362. https://doi.org/10.1038/s41586-020-2649-2 DOI: https://doi.org/10.1038/s41586-020-2649-2
Lei, F., Liu, X., Dai, Q., and Ling, B. W. K. (2020). Shallow Convolutional Neural Network for Image Classification. SN Applied Sciences, 2(1), Article 1–8. https://doi.org/10.1007/s42452-019-1903-4 DOI: https://doi.org/10.1007/s42452-019-1903-4
Leithardt, V. (2021). Classifying Garments from the Fashion-MNIST Dataset through CNNs. Advances in Science, Technology and Engineering Systems Journal, 6(3), 989–994. https://doi.org/10.25046/aj0601109 DOI: https://doi.org/10.25046/aj0601109
Messer, U. (2024). Co-creating Art with Generative Artificial Intelligence: Implications for Artworks and Artists. Computers in Human Behavior: Artificial Humans, 2, Article 100056. https://doi.org/10.1016/j.chbah.2024.100056 DOI: https://doi.org/10.1016/j.chbah.2024.100056
Schaerf, L., Postma, E., and Popovici, C. (2024). Art Authentication with Vision Transformers. Neural Computing and Applications, 36, 11849–11858. https://doi.org/10.1007/s00521-023-08864-8 DOI: https://doi.org/10.1007/s00521-023-08864-8
Tang, Y., Cui, H., and Liu, S. (2020). Optimal Design of Deep Residual Network Based on Image Classification of Fashion-MNIST Dataset. Journal of Physics: Conference Series, 1624, Article 052011. https://doi.org/10.1088/1742-6596/1624/5/052011 DOI: https://doi.org/10.1088/1742-6596/1624/5/052011
Wang, J., et al. (2021). Milvus: A Purpose-Built Vector Data Management System. In Proceedings of the ACM SIGMOD International Conference on Management of Data (2614–2627). https://doi.org/10.1145/3448016.3457550 DOI: https://doi.org/10.1145/3448016.3457550
Zeng, Z., Zhang, P., Qiu, S., Li, S., and Liu, X. (2024). A Painting Authentication Method Based on Multi-Scale Spatial-Spectral Feature Fusion and Convolutional Neural Network. Computers and Electrical Engineering, 118, Article 109315. https://doi.org/10.1016/j.compeleceng.2024.109315 DOI: https://doi.org/10.1016/j.compeleceng.2024.109315
Zhang, Z., Sun, P., Jiang, Y., Yu, D., Weng, F., Yuan, Z., Luo, P., and Liu, W. (2021). Conditional DETR: A Modularized DETR Framework for Object Detection. arXiv.
Zhao, S., Li, Y., Wang, J., Liu, Y., and Zhang, X. (2024). Efficient Construction and Convergence Analysis of Sparse Convolutional Neural Networks. Neurocomputing, 597, Article 128032. https://doi.org/10.1016/j.neucom.2024.128032 DOI: https://doi.org/10.1016/j.neucom.2024.128032
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Paramjit Baxi, Ms. Saritha Sr, Praveen Kumar Tomar, Harshita Sharma, Smitha K, Wamika Goyal, Ashutosh Kulkarni
This work is licensed under a Creative Commons Attribution 4.0 International License.
With the licence CC-BY, authors retain the copyright, allowing anyone to download, reuse, re-print, modify, distribute, and/or copy their contribution. The work must be properly attributed to its author.
It is not necessary to ask for further permission from the author or journal board.
This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.
