A Similarity Measure for Weaving Patterns in Textiles

TL;DR

This paper introduces a new similarity measure for weaving patterns in textiles, utilizing hypergraph representations and set-based comparisons to enable efficient search and clustering in textile archives.

Contribution

It presents a novel hypergraph-based approach for measuring textile pattern similarity, combining multiple comparison metrics and demonstrating practical effectiveness.

Findings

Efficient implementation of the similarity measure.

Successful clustering and querying of over a thousand textile samples.

Effective comparison of weaving patterns using multiple metrics.

Abstract

We propose a novel approach for measuring the similarity between weaving patterns that can provide similarity-based search functionality for textile archives. We represent textile structures using hypergraphs and extract multisets of k-neighborhoods from these graphs. The resulting multisets are then compared using Jaccard coefficients, Hamming distances, and cosine measures. We evaluate the different variants of our similarity measure experimentally, showing that it can be implemented efficiently and illustrating its quality using it to cluster and query a data set containing more than a thousand textile samples.