# Non-Invasive Painting Pigment Classification Through Supervised Machine Learning
## 2 Related Work  
Recent advancements in pigment classification have focused on improving cultural heritage analysis by replacing invasive techniques with imaging-based alternatives. Traditional methods such as X-ray fluorescence (XRF), Raman spectroscopy, and Fourier transform infrared spectroscopy (FTIR) are highly accurate but can be spatially limited and time-consuming or require specific conditions that make them impractical for routine, large-scale, or in situ analysis of complex artworks. To address these limitations, non-invasive approaches such as Visible Spectral Imaging (VSI) and Hyperspectral Imaging (HSI) have been developed [3; 9; 10]. Early computational techniques for analyzing such data included Spectral Angle Mapping (SAM) and Principal Component Analysis (PCA), which proved effective in simplified scenarios but are often insufficient for dealing with complex pigment mixtures [11; 12]. Machine learning and deep learning methodologies have since emerged as powerful tools capable of addressing these challenges. Notably, [PERSON] et al. [13] introduced a deep neural network (DNN) for pigment classification in the short-wave infrared range, achieving robust multi-label performance on complex surfaces such as Tibetan thangkas. Their method significantly outperforms SAM in both speed and accuracy. Similarly, [PERSON] et al. [11] reviewed the growing application of neural networks in hyperspectral pigment analysis, emphasizing their ability to model nonlinear relationships and manage high-dimensional spectral data effectively. [PERSON] et al. [14] approached the problem from a perceptual perspective, training networks to replicate human color constancy under variable lighting conditions using synthetic data. Their DeepCC model achieved strong performance in reflectance classification tasks, demonstrating the broader applicability of biologically inspired models in cultural heritage imaging. Within the domain of visible spectral imaging, [PERSON] et al. [9] proposed a multi-stage framework incorporating boundary segmentation, material recognition, and mixture proportion estimation. Their results highlighted the diagnostic potential of reflectance spectra while acknowledging limitations caused by pigment particle variability and spectral mixing. Expanding on this work, [PERSON] et al. [10] developed a quadtree-based superpixel segmentation algorithm combined with geometric spectral descriptors for database-driven pigment identification, successfully automating region-level classification in polychrome surfaces. Reflectance unmixing strategies have also been explored. [PERSON] et al. [12] demonstrated that transforming reflectance data into the logarithmic domain (\(-\log(R)\)) can enhance the performance of linear unmixing models, particularly in aged or stratified paint layers. The present study builds on these developments by proposing a lightweight classification strategy that leverages three distinct illumination modalities--visible, ultraviolet, and infrared--to extract compact statistical features from reflectographic images. This design significantly reduces the complexity of data acquisition and processing in comparison to full-spectrum hyperspectral imaging, offering a practical and efficient solution for use in conservation contexts. Further contributions to scalable pigment analysis have emerged from the combination of deep learning and simulation-based workflows. [PERSON] et al. [15] employed Kohonen Self-Organizing Maps (SOMs) to cluster high-resolution reflectance imaging data from mural paintings in the Mogao Caves, enabling the identification of materials in both accessible and inaccessible regions. Their results underscore the potential of unsupervised learning in processing large-scale cultural heritage datasets. In the field of XRF imaging, [PERSON] et al. [16] addressed the scarcity of training data by generating synthetic pigment spectra using the Fundamental Parameters (FP) method.