基于超图的结构纺织品模式识别与处理
Structural Textile Pattern Recognition and Processing Based on Hypergraphs
摘要：
人文学科与社会的许多其他领域一样，目前正在经历数字化转型带来的重大变化。然而，为了使这一领域的数字化材料更容易获得，我们往往仍然缺乏足够的搜索功能。例如，纺织品的数字档案提供了关键词搜索，这是相当好理解的，并按照一定的分类法排列其内容，但在线程结构层面的搜索功能仍然缺失。为了便于聚类和搜索，我们介绍了一种方法，用于识别相似的编织模式的基础上，他们的结构纺织档案。我们首先代表纺织结构，使用超图和提取多组的k-邻域描述编织图案从这些图。然后，使用各种距离度量和各种聚类算法（简单的K-Means和精确的分层凝聚算法）对所得到的多集进行聚类。我们通过实验评估了我们方法的不同变体，表明这可以有效地实现（这意味着它具有线性复杂性），并证明了它对包含大型纺织品样本的查询和聚类数据集的质量。据我们所知，这是第一个实用的方法，明确建模复杂和不规则的编织模式可用于检索，我们的目标是建立一个坚实的基线。
英文摘要：
The humanities, like many other areas of society, are currently undergoing major changes in the wake of digital transformation. However, in order to make collection of digitised material in this area easily accessible, we often still lack adequate search functionality. For instance, digital archives for textiles offer keyword search, which is fairly well understood, and arrange their content following a certain taxonomy, but search functionality at the level of thread structure is still missing. To facilitate the clustering and search, we introduce an approach for recognising similar weaving patterns based on their structures for textile archives. We first represent textile structures using hypergraphs and extract multisets of k-neighbourhoods describing weaving patterns from these graphs. Then, the resulting multisets are clustered using various distance measures and various clustering algorithms (K-Means for simplicity and hierarchical agglomerative algorithms for precision). We evaluate the different variants of our approach experimentally, showing that this can be implemented efficiently (meaning it has linear complexity), and demonstrate its quality to query and cluster datasets containing large textile samples. As, to the est of our knowledge, this is the first practical approach for explicitly modelling complex and irregular weaving patterns usable for retrieval, we aim at establishing a solid baseline.
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