  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.
