WESD - Weighted Spectral Distance for Measuring Shape Dissimilarity
This work provides a mathematically grounded distance metric for shape comparison, primarily benefiting researchers in computer vision and medical imaging, though it appears incremental as it builds on existing eigenvalue-based descriptors.
The authors tackled the problem of measuring shape dissimilarity by introducing the Weighted Spectral Distance (WESD), a new distance derived from eigenvalues of the Laplace operator, which is proven to converge and be approximated with finite eigenvalues, with experiments showing practical benefits in vision and medical image analysis.
This article presents a new distance for measuring shape dissimilarity between objects. Recent publications introduced the use of eigenvalues of the Laplace operator as compact shape descriptors. Here, we revisit the eigenvalues to define a proper distance, called Weighted Spectral Distance (WESD), for quantifying shape dissimilarity. The definition of WESD is derived through analysing the heat-trace. This analysis provides the proposed distance an intuitive meaning and mathematically links it to the intrinsic geometry of objects. We analyse the resulting distance definition, present and prove its important theoretical properties. Some of these properties include: i) WESD is defined over the entire sequence of eigenvalues yet it is guaranteed to converge, ii) it is a pseudometric, iii) it is accurately approximated with a finite number of eigenvalues, and iv) it can be mapped to the [0,1) interval. Lastly, experiments conducted on synthetic and real objects are presented. These experiments highlight the practical benefits of WESD for applications in vision and medical image analysis.