@article{M9C338449,
title = "An Empirical Analysis of Domain Bias In Internet Image-Based Gemstone Identification Systems",
journal = "Journal of KIISE, JOK",
year = "2025",
issn = "2383-630X",
doi = "10.5626/JOK.2025.52.11.970",
author = "Choolha Hwang, Dongha Shim",
keywords = "gemstone identification, computer vision, domain bias, domain generalization, domain adaptation, image enhancement",
abstract = "Recent applications of machine learning and computer vision in gemstone identification increasingly utilize internet images as training data. However, commercial enhancements such as color correction, contour sharpening, and shape distortion-create visual discrepancies that result in domain bias,  significantly degrading model performance in real-world environments. This study empirically analyzes this issue by designing nine training-evaluation scenarios. using three distinct datasets: academic (Set A), public internet (Set B), and directly captured unprocessed images (Set C). The results indicate that models trained on Set A experienced a 26% drop in accuracy when evaluated on Set C. In contrast, models trained on Set C maintained stable performance (F1 Score ≥ 0.83) when tested on Set A and Set B. These findings underscore the critical impact of visual discrepancies on model generalization and highlight the necessity of training with unprocessed real-world images to address domain bias for reliable AI-based gemstone identification systems."
}