Geometric Similarity

Geometric similarity focuses on quantifying the resemblance between shapes or patterns, a crucial task across diverse fields from neuroscience to music information retrieval. Current research emphasizes developing statistically robust methods for estimating shape distances, particularly in high-dimensional spaces and data-scarce scenarios, often employing novel estimators and incorporating concepts like conformal prediction for reliable results. These advancements are improving the accuracy and reliability of applications ranging from animal re-identification using image analysis to enhancing the performance of few-shot learning algorithms by prioritizing feature ranking over traditional geometric similarity metrics.