Graph-based Strategy for Establishing Morphology Similarity

Analysis of morphological data is central to a broad class of scientific problems in materials science, astronomy, bio-medicine, and many others. Understanding relationships between morphologies is a core analytical task in such settings. In this paper, we propose a graph-based framework for measuring similarity between morphologies. Our framework delivers a novel representation of a morphology as an augmented graph that encodes application-specific knowledge through the use of configurable signature functions. It provides also an algorithm to compute the similarity between a pair of morphology graphs. We present experimental results in which the framework is applied to morphology data from high-fidelity numerical simulations that emerge in materials science. The results demonstrate that our proposed measure is superior in capturing the semantic similarity between morphologies, compared to the state-of-the-art methods such as FFT-based measures.