## 1 Introduction  
### Overview  
Pigments, essential materials in the conservation of cultural heritage and painting, are produced through both industrial synthesis and natural extraction from minerals. Distinguishing between these two production methods is challenging through visual and chemical composition analyses alone. Studies estimating the manufacturing process of pigments with identical chemical formulas and structures typically focus on classifying the crystalline form of the particles. Identifying the crystal form or microstructure of particles is typically performed using data derived from microscopes and polarized light microscopic image data atlases. However, there is a limited accumulation of microscopic image data, such as atlases. Furthermore, analyzing these micro-structured images requires experts to manually review hundreds of individual images from a single sample, confirming common particle shapes [1]. This process is time-consuming and heavily relies on the expertise of the observer.  
Discrimination models have been developed using convolutional neural networks (CNNs), such as AlexNet, GoogLeNet, ResNet, and VGG, to classify pigment-manufacturing processes with over 96% accuracy [2; 3]. CNNs, which are highly effective for object recognition, allow for objective and quantitative analysis of large-scale image data. Recent studies have applied CNNs across various fields [4]. In cultural heritage, CNNs are primarily used for image classification of architectural heritage and archeological topography [5; 6; 7]. Additionally, CNNs (e.g., VGG16, InceptionNet V3, ResNet50, and EfficientNet B0) have been used to evaluate damage in digital single-lens reflex images of wooden architectural heritage artifacts, visualizing the location of damage through a class activation map (CAM) [8]. In cultural heritage and image classification, large-scale spectrum libraries are used to fine-tune CNNs and identify material components [9; 10; 11]. Accurate identification of constituent elements through CNN classification techniques is crucial for the conservation of cultural heritage. When conserving and repairing cultural heritage, the principles of the Venice Charter are followed, and traditional materials are prioritized [12]. Cultural heritage sites involving pigments, such as paintings and murals, typically rely on natural mineral pigments. However, the advancement of industrial pigments since the 19 th century has hindered the visual identification of pigments produced by these two methods. For example, distinguishing between red pigments like vermilion and cinnabar requires observing the fine shape of pigment particles and matching their chemical composition (i.e., Hg and S) [1; 13]. A previous study successfully categorized pigment-manufacturing processes with over 96% accuracy using CNNs [2].  
While CNNs have shown high classification accuracy in cultural heritage applications, their lack of interpretability and failure to suitably capture complex patterns hinder their broader applicability. Recent advancements in vision transformers (ViTs), which can efficiently process such complex data, provide an opportunity to bridge these gaps and enhance both performance and interoperability. Given its recent development, however, the application of ViTs to cultural heritage preservation remains largely unexplored.  
We aim to investigate the effectiveness of ViTs for pigment-manufacturing classification, comparing them to established CNNs and addressing key challenges in both accuracy and interpretability. By training interpretable filters to identify patterns in the input images that influence predictions, we advance the classification of pigment production processes, building upon previous studies, and propose an interpretable deep learning method.