  This short paper presents preliminary research on the Case-Enhanced Vision
Transformer (CEViT), a similarity measurement method aimed at improving the
explainability of similarity assessments for image data. Initial experimental
results suggest that integrating CEViT into k-Nearest Neighbor (k-NN)
classification yields classification accuracy comparable to state-of-the-art
computer vision models, while adding capabilities for illustrating differences
between classes. CEViT explanations can be influenced by prior cases, to
illustrate aspects of similarity relevant to those cases.
