PATTERN RECOGNITION IN TRIBAL ART USING CNN MODELS - Molawade, Mayuri H; Thakuriya, Kajal; Khanna, Lalit; Mahajan, Sulabh; Tomar, Praveen Kumar; Debnath, Baisakhi; Itnal, Vijay (ShodhKosh: Journal of Visual and Performing Arts, 2025)
The identification and categorization of tribal art is a challenging issue because of the many cultural styles, sophisticated designs and differences in art performance among the people. The conventional methods of image recognition do not always have the ability to depict the visual complexity of tribal art. Due to recent progress in deep learning, Convolutional Neural Networks (CNNs) have proven to be unusually capable of automatically learning hierarchical visual-based features on images, thus being applicable to intricate pattern recognition problems. This paper examines how three common CNN construction models, namely VGG16, ResNet and Inception, can be used to recognize and classify tribal art patterns. An augmented and normalized dataset of various types of tribal art, obtained via web-based repositories, cultural archives, and field photography, is pre-processed by augmentation and normalization in order to increase model generalization. The CNN models are trained and fine-tuned to identify the unique low-level and high-level features which are tribal motifs, geometric structures, and stylistic features. The performance of the model is measured using the accuracy, visualization of the feature-map, and a comparison to traditional recognition processes. Findings show that deep CNN models are far much better than classic ones as they detect complex textures and patterns with greater accuracy. The discussion reveals the topicality of these discoveries in justifying the digital humanities projects, such as preserving, classifying, and authenticating the cultural heritage of tribal communities.