Scale-invariant heat kernel signatures for non-rigid shape recognition

One of the biggest challenges in non-rigid shape retrieval and comparison is the design of a shape descriptor that would maintain invariance under a wide class of transformations the shape can undergo. Recently, heat kernel signature was introduced as an intrinsic local shape descriptor based on dif...

Full description

Saved in:
Bibliographic Details
Published in2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition pp. 1704 - 1711
Main Authors Bronstein, M M, Kokkinos, I
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2010
Subjects
Computer vision
Extraterrestrial measurements
Geometry
Image analysis
Image retrieval
Image sampling
Information retrieval
Internet
Kernel
Shape
Online AccessGet full text

Cover

More Information
Summary:One of the biggest challenges in non-rigid shape retrieval and comparison is the design of a shape descriptor that would maintain invariance under a wide class of transformations the shape can undergo. Recently, heat kernel signature was introduced as an intrinsic local shape descriptor based on diffusion scale-space analysis. In this paper, we develop a scale-invariant version of the heat kernel descriptor. Our construction is based on a logarithmically sampled scale-space in which shape scaling corresponds, up to a multiplicative constant, to a translation. This translation is undone using the magnitude of the Fourier transform. The proposed scale-invariant local descriptors can be used in the bag-of-features framework for shape retrieval in the presence of transformations such as isometric deformations, missing data, topological noise, and global and local scaling. We get significant performance improvement over state-of-the-art algorithms on recently established non-rigid shape retrieval benchmarks.
ISBN:1424469848
9781424469840
ISSN:1063-6919
DOI:10.1109/CVPR.2010.5539838