An Explainable and Robust Framework for Telkari Jewelry Recognition Using Deep Feature Representations and ANOVA-Based Feature Selection

Telkari is a decorative art based on the handcrafting of fine silver wires. Identifying Telkari jewelry is quite challenging due to its diverse designs and styles. Recognizing these jewelry items, which come in thousands of varieties, requires experts in Telkari. In this study, we propose an approach for Telkari recognition using computer vision techniques, aiming to simulate the analysis of Telkari jewelry by Telkari experts. In the first stage, we created a dataset of Telkari by collecting images of Telkari products produced by Telkari masters in the Midyat district of Mardin, Turkey. In the second stage, the performance of the MobileNetV2 and ResNet50 deep learning models was examined using both direct classification and hyperparameter-tuned approaches. Furthermore, the feature vectors extracted from the deep learning models were trained using traditional machine learning algorithms, SVM, KNN, XGB, and RF, after selecting discriminative features with ANOVA. Among the hyperparameter-tuned models, ResNet50 outperformed MobileNetV2. Among the hybrid approaches, the SVM model trained on features obtained from ResNet50 achieved the highest performance with an overall accuracy of 99.56%. Furthermore, the interpretability techniques GradCAM, t-SNE, and LIME were used to examine the model’s decision-making processes, confirming that they focused on relevant visual regions and that predictions were based on the complex interactions among multiple features. In conclusion, this study provides a robust methodology for developing a highly accurate and transparent classification system for fields such as cultural heritage preservation and digitization.