A Similarity Measure for Weaving Patterns in Textiles
This work addresses a domain-specific problem for textile archives by enabling similarity-based search, but it is incremental as it applies existing graph and set comparison methods to a new application area.
The paper tackles the problem of measuring similarity between weaving patterns for textile archives by representing textile structures as hypergraphs and comparing extracted multisets using various metrics, achieving efficient implementation and demonstrating quality through clustering and querying over a thousand textile samples.
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.