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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| Vydané v: | 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition s. 1704 - 1711 |
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| Hlavní autori: | , |
| Médium: | Konferenčný príspevok.. |
| Jazyk: | English |
| Vydavateľské údaje: |
IEEE
01.06.2010
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| Predmet: | |
| ISBN: | 1424469848, 9781424469840 |
| ISSN: | 1063-6919, 1063-6919 |
| On-line prístup: | Získať plný text |
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| Shrnutí: | 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. |
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| ISBN: | 1424469848 9781424469840 |
| ISSN: | 1063-6919 1063-6919 |
| DOI: | 10.1109/CVPR.2010.5539838 |