  With the proliferation of image-based applications in various domains, the
need for accurate and interpretable image similarity measures has become
increasingly critical. Existing image similarity models often lack
transparency, making it challenging to understand the reasons why two images
are considered similar. In this paper, we propose the concept of explainable
image similarity, where the goal is the development of an approach, which is
capable of providing similarity scores along with visual factual and
counterfactual explanations. Along this line, we present a new framework, which
integrates Siamese Networks and Grad-CAM for providing explainable image
similarity and discuss the potential benefits and challenges of adopting this
approach. In addition, we provide a comprehensive discussion about factual and
counterfactual explanations provided by the proposed framework for assisting
decision making. The proposed approach has the potential to enhance the
interpretability, trustworthiness and user acceptance of image-based systems in
real-world image similarity applications. The implementation code can be found
in https://github.com/ioannislivieris/Grad_CAM_Siamese.git.
