VEMOS: A GUI for Evaluation of Similarity Metrics on Complex Data Sets

Published

June 7, 2021

Author(s)

Gunay Dogan, Eve Fleisig

Abstract

Similarity and dissimilarity metrics are a fundamental component of many tasks requiring the analysis and comparison of complex, often visual data. Applications from deep learning to forensics require ways to effectively identify images, find clusters or outliers in data sets, or retrieve data items similar to a query item. However, finding an effective metric for a specific task is challenging due to the complexity of modern datasets and the myriad possible similarity metrics arising from that complexity. We present VEMOS, a Python package that provides an accessible graphical user interface (GUI) for the evaluation of comparison metrics. VEMOS provides user-friendly ways to examine individual data items or groups in a data set alongside analyses of metrics' performance on the whole data set, such as clustering, multi-dimensional scaling, and retrieval performance analyses. VEMOS aims to help researchers and practitioners evaluate multiple comparison metrics on rich, diverse data sets.

Citation

Technical Note (NIST TN) - 2160

Report Number

2160

NIST Pub Series

Technical Note (NIST TN)

Pub Type

NIST Pubs

Download Paper

https://doi.org/10.6028/NIST.TN.2160

Local Download

Keywords

GUI, similarity metric, distance metric, data visualization, data retrieval

Citation

Dogan, G. and Fleisig, E. (2021), VEMOS: A GUI for Evaluation of Similarity Metrics on Complex Data Sets, Technical Note (NIST TN), National Institute of Standards and Technology, Gaithersburg, MD, [online], https://doi.org/10.6028/NIST.TN.2160, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=928654 (Accessed May 4, 2026)

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