用深度学习比画布纹理，让名画真伪鉴定更准更快
Forensic Study of Paintings Through the Comparison of Fabrics
- 用孪生网络提取画布图像特征，自动比对纹理相似性
- 在普拉多博物馆画作上验证，准确识别相似织物
- 无需线密度图，适合不连续画布的鉴定，适合艺术鉴定师
艺术品画布纺织品研究是鉴定真伪、归属和保护的关键手段。传统方法依赖线密度图匹配，但在画布非连续取样时无法应用。本文提出一种基于深度学习的新方法，通过自动评估画布间相似性，无需依赖线密度图。设计并训练了一个孪生深度学习模型，利用扫描图像的特征表示比较成对画布。此外，提出一种相似性估计方法，整合多个画布样本对的预测结果，提供稳健的相似性评分。该方法应用于普拉多国家博物馆的画布，证实即使线密度相似，平纹画布仍可有效比对。结果表明该方法可行且准确，为大师级作品分析开辟新路径。
原文摘要 · Abstract (English)
The study of canvas fabrics in works of art is a crucial tool for authentication, attribution and conservation. Traditional methods are based on thread density map matching, which cannot be applied when canvases do not come from contiguous positions on a roll. This paper presents a novel approach based on deep learning to assess the similarity of textiles. We introduce an automatic tool that evaluates the similarity between canvases without relying on thread density maps. A Siamese deep learning model is designed and trained to compare pairs of images by exploiting the feature representations learned from the scans. In addition, a similarity estimation method is proposed, aggregating predictions from multiple pairs of cloth samples to provide a robust similarity score. Our approach is applied to canvases from the Museo Nacional del Prado, corroborating the hypothesis that plain weave canvases, widely used in painting, can be effectively compared even when their thread densities are similar. The results demonstrate the feasibility and accuracy of the proposed method, opening new avenues for the analysis of masterpieces.
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