# Non-Invasive Painting Pigment Classification Through Supervised Machine Learning
## 5 Results
The classifier trained on laboratory data was applied to these patches using the same feature extraction and prediction pipeline. Results presented in Table 3 show that, within homogeneous regions, the subclass-based classifier reached top-10 accuracy values of up to 100%, demonstrating the system's potential to generalize effectively beyond controlled datasets. Despite this strong performance, certain limitations were observed. Misclassification occurred in cases where pigments had similar spectral profiles, such as ultramarine blue and \begin{table}
\begin{tabular}{l c c c c} \hline
**Classifier** & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline
**Random Forest** & **99.30 \(\pm\) 0.15** & **99.33 \(\pm\) 0.14** & **99.30 \(\pm\) 0.15** & **99.29 \(\pm\) 0.15** \\ Adaboost & 54.26 \(\pm\) 2.88 & 55.86 \(\pm\) 3.00 & 54.26 \(\pm\) 2.88 & 50.30 \(\pm\) 3.05 \\ Decision Tree & 5.83 \(\pm\) 0.02 & 4.22 \(\pm\) 0.13 & 5.83 \(\pm\) 0.02 & 4.53 \(\pm\) 0.09 \\ Logistic Regression & 69.03 \(\pm\) 0.48 & 67.05 \(\pm\) 0.35 & 69.03 \(\pm\) 0.48 & 63.71 \(\pm\) 0.46 \\ SGD Classifier & 23.82 \(\pm\) 3.95 & 18.68 \(\pm\) 3.39 & 23.82 \(\pm\) 3.95 & 18.12 \(\pm\) 3.27 \\ SVM Classifier & 90.00 \(\pm\) 0.43 & 91.88 \(\pm\) 0.53 & 90.00 \(\pm\) 0.43 & 89.34 \(\pm\) 0.49 \\ KNN Classifier & 99.26 \(\pm\) 0.12 & 99.30 \(\pm\) 0.12 & 99.26 \(\pm\) 0.12 & 99.26 \(\pm\) 0.12 \\ Gaussian NB & 96.60 \(\pm\) 0.15 & 96.90 \(\pm\) 0.14 & 96.60 \(\pm\) 0.15 & 96.61 \(\pm\) 0.16 \\ Bagging Classifier & 44.05 \(\pm\) 2.96 & 39.59 \(\pm\) 2.08 & 44.05 \(\pm\) 2.96 & 39.92 \(\pm\) 2.33 \\ Extra Trees & 94.25 \(\pm\) 0.37 & 95.04 \(\pm\) 0.32 & 94.25 \(\pm\) 0.37 & 93.87 \(\pm\) 0.45 \\ \hline \end{tabular}
\end{table} 
Table 2: Cross-validation results of selected classification algorithms. \begin{table}
\begin{tabular}{l c c c} \hline
**Approach** & **Top-1** & **Top-3** & **Top-10** \\ \hline Unified-class approach & 25.0\% & 46.9\% & 68.8\% \\ Subclass-based approach & 25.0\% & 53.1\% & 78.1\% \\ \hline Unified class (high-confidence subset) & 34.8\% & 65.3\% & **95.7\%** \\ Subclass-based (high-confidence subset) & 32.0\% & 68.0\% & **100.0\%** \\ \hline \end{tabular}
\end{table} 
Table 3: Accuracy of pigment classification on real painting samples. Prussian blue, due to overlapping reflectance across the three imaging modalities: visible reflectance, ultraviolet false-color, and infrared false-color modalities. Additionally, organic lakes and degraded materials, which often exhibit minimal spectral contrast, proved more difficult to identify reliably. These issues highlight the limited spectral discrimination afforded by the current imaging setup. This scenario reflects a trade-off between the simplicity of the imaging protocol and the level of diagnostic precision. While the use of three imaging modalities offers a low-complexity and accessible workflow, it may be insufficient for distinguishing between pigments with overlapping visual or spectral characteristics. Future improvements could include the incorporation of multi-label classification frameworks or spectral unmixing techniques to enhance performance in more complex or ambiguous cases. These findings demonstrate that the classification framework, particularly when using ensemble methods and subclass-based labeling, achieves high accuracy and generalization capacity across both experimental and real-world data, supporting its applicability in conservation-focused pigment analysis.