Structural textile pattern recognition and processing based on hypergraphs

Abstract

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 best 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.

Analyzing Shared Bike Usage Through Graph-Based Spatio-Temporal Modeling

“…We successfully utilized a graph network to model the structure of textile patterns [19] and a historical knowledge base [20]. Consequently, we constructed a Spatial Bike Graph Network (SBiGN) based on the SGN to describe the spatial features of the bike sharing usage, including station connections and trip volumes between stations.…”

Graph-Based Optimisation of Network Expansion in a Dockless Bike Sharing System

“…One of the primary objectives of this endeavor is to identify the optimal number of stations in suitable locations, a task that necessitates spatial analysis employing appropriate tools and methods. Graph networks have been widely utilized in various applications, including textile structure [5], [6], ontology [7], [8], [9], knowledgebased systems [10], [11] and electronic health records [12]. In the context of bike-sharing systems, graph-based approaches have been employed to model locations, trips, and associated time intervals, facilitating the investigation of system dynamics.…”