Textiles have an important role in many cultures and have been digitised. They are three-dimensional objects and have complex structures, especially archaeological fabric specimens and artifact textiles created manually by traditional craftsmen. In this article, we propose a novel algorithm for textile classification based on their structures. First, a hypergraph is used to represent the textile structure. Second, multisets of k -neighbourhoods are extracted from the hypergraph and converted to one feature vector for representation of each textile. Then, the k -neighbourhood vectors are classified using seven most popular supervised learning methods. Finally, we evaluate experimentally the different variants of our approach on a data set of 1,600 textile samples with the 4-fold cross-validation technique. The experimental results indicate that comparing the variants, the best classification accuracies are 0.999 with LR, 0.994 with LDA, 0.996 with KNN, 0.994 with CART, 0.998 with NB, 0.974 with SVM, and 0.999 with NNM.

中文翻译：

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一种基于结构的不同文化传统纺织图案的高效分类算法

纺织品在许多文化中都发挥着重要作用，并已被数字化。它们是三维物体，结构复杂，尤其是由传统工匠手工制作的考古织物标本和人工纺织品。在本文中，我们提出了一种基于织物结构的纺织品分类新算法。首先，使用超图来表示纺织品结构。二、多组ķ- 从超图中提取邻域并将其转换为一个特征向量以表示每种纺织品。然后，ķ- 使用七种最流行的监督学习方法对邻域向量进行分类。最后，我们使用 4 折交叉验证技术在 1,600 个纺织品样本的数据集上对我们方法的不同变体进行了实验评估。实验结果表明，比较变体，最佳分类准确率分别是 LR 为 0.999、LDA 为 0.994、KNN 为 0.996、CART 为 0.994、NB 为 0.998、SVM 为 0.974、NNM 为 0.999。