CatSIM: A Categorical Image Similarity Metric

Abstract:We introduce CatSIM, a new similarity metric for binary and multinary two- and three-dimensional images and volumes. CatSIM uses a structural similarity image quality paradigm and is robust to small perturbations in location so that structures in similar, but not entirely overlapping, image or volumetric regions are rated higher than by simple matching. The metric can also compare arbitrary regions inside images and volumes. CatSIM is evaluated on artificial data sets, validated by comparing with human perception in two separate image quality assessment surveys, and illustrated on two datasets. The publicly available R package \texttt{catsim} implements the methodology.

Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Image and Video Processing (eess.IV); Machine Learning (stat.ML)

arXiv:2004.09073 [cs.CV]

(or arXiv:2004.09073v2 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2004.09073 Focus to learn more arXiv-issued DOI via DataCite

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.