title: WESD--Weighted Spectral Distance for measuring shape dissimilarity.
abstract: This paper 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 analyzing the heat trace. This analysis provides the proposed distance with an intuitive meaning and mathematically links it to the intrinsic geometry of objects. We analyze the resulting distance definition, present and prove its important theoretical properties. Some of these properties include: 1) WESD is defined over the entire sequence of eigenvalues yet it is guaranteed to converge, 2) it is a pseudometric, 3) it is accurately approximated with a finite number of eigenvalues, and 4) it can be mapped to the [0,1) interval. Last, 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.
authors.affiliation: Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital/Harvard Medical School, Cambridge, MA 02144, USA. ender.konukoglu@gmail.com
authors.forename: Ender
authors.initials: E
authors.lastname: Konukoglu
authors.forename: Ben
authors.initials: B
authors.lastname: Glocker
authors.forename: Antonio
authors.initials: A
authors.lastname: Criminisi
authors.forename: Kilian M
authors.initials: KM
authors.lastname: Pohl
mesh_terms: D000465:Algorithms; D000818:Animals; D001921:Brain / Q000033:anatomy & histology; D057791:Cardiac Imaging Techniques; D003198:Computer Simulation; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging; D010363:Pattern Recognition, Automated / Q000379:methods*