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...

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Veröffentlicht in:2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition S. 1704 - 1711
Hauptverfasser: Bronstein, M M, Kokkinos, I
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.06.2010
Schlagworte:
Computer vision
Extraterrestrial measurements
Geometry
Image analysis
Image retrieval
Image sampling
Information retrieval
Internet
Kernel
Shape
ISBN:1424469848, 9781424469840
ISSN:1063-6919, 1063-6919
Online-Zugang:Volltext
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Zusammenfassung: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
1063-6919
DOI:10.1109/CVPR.2010.5539838