Shape recognition with spectral distances

Michael M. Bronstein; Alexander M. Bronstein

doi:https://doi.org/10.1109/TPAMI.2010.210

Shape recognition with spectral distances

Michael M. Bronstein, Alexander M. Bronstein

Research output: Contribution to journal › Article › peer-review

Abstract

Recent works have shown the use of diffusion geometry for various pattern recognition applications, including nonrigid shape analysis. In this paper, we introduce spectral shape distance as a general framework for distribution-based shape similarity and show that two recent methods for shape similarity due to Rustamov and Mahmoudi and Sapiro are particular cases thereof.

Original language	English
Article number	5661779
Pages (from-to)	1065-1071
Number of pages	7
Journal	IEEE Transactions on Pattern Analysis and Machine Intelligence
Volume	33
Issue number	5
DOIs	https://doi.org/10.1109/TPAMI.2010.210
State	Published - 2011
Externally published	Yes

Keywords

Diffusion distance
Laplace-Beltrami operator
commute time
distribution
eigenmap
global point signature
heat kernel
nonrigid shapes
similarity
spectral distance

All Science Journal Classification (ASJC) codes

Software
Computer Vision and Pattern Recognition
Computational Theory and Mathematics
Artificial Intelligence
Applied Mathematics

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https://doi.org/10.1109/TPAMI.2010.210

Cite this

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title = "Shape recognition with spectral distances",

abstract = "Recent works have shown the use of diffusion geometry for various pattern recognition applications, including nonrigid shape analysis. In this paper, we introduce spectral shape distance as a general framework for distribution-based shape similarity and show that two recent methods for shape similarity due to Rustamov and Mahmoudi and Sapiro are particular cases thereof.",

keywords = "Diffusion distance, Laplace-Beltrami operator, commute time, distribution, eigenmap, global point signature, heat kernel, nonrigid shapes, similarity, spectral distance",

author = "Bronstein, {Michael M.} and Bronstein, {Alexander M.}",

note = "Funding Information: The authors are grateful to Ron Kimmel, Radu Horaud, and Guillermo Sapiro for insightful discussions. This research was supported in part by the Office of Naval Research Grant.",

year = "2011",

doi = "https://doi.org/10.1109/TPAMI.2010.210",

language = "الإنجليزيّة",

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T1 - Shape recognition with spectral distances

AU - Bronstein, Michael M.

AU - Bronstein, Alexander M.

N1 - Funding Information: The authors are grateful to Ron Kimmel, Radu Horaud, and Guillermo Sapiro for insightful discussions. This research was supported in part by the Office of Naval Research Grant.

PY - 2011

Y1 - 2011

N2 - Recent works have shown the use of diffusion geometry for various pattern recognition applications, including nonrigid shape analysis. In this paper, we introduce spectral shape distance as a general framework for distribution-based shape similarity and show that two recent methods for shape similarity due to Rustamov and Mahmoudi and Sapiro are particular cases thereof.

AB - Recent works have shown the use of diffusion geometry for various pattern recognition applications, including nonrigid shape analysis. In this paper, we introduce spectral shape distance as a general framework for distribution-based shape similarity and show that two recent methods for shape similarity due to Rustamov and Mahmoudi and Sapiro are particular cases thereof.

KW - Diffusion distance

KW - Laplace-Beltrami operator

KW - commute time

KW - distribution

KW - eigenmap

KW - global point signature

KW - heat kernel

KW - nonrigid shapes

KW - similarity

KW - spectral distance

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U2 - https://doi.org/10.1109/TPAMI.2010.210

DO - https://doi.org/10.1109/TPAMI.2010.210

M3 - مقالة

SN - 0162-8828

VL - 33

SP - 1065

EP - 1071

JO - IEEE Transactions on Pattern Analysis and Machine Intelligence

JF - IEEE Transactions on Pattern Analysis and Machine Intelligence

IS - 5

M1 - 5661779

ER -

Shape recognition with spectral distances

Abstract

Keywords

All Science Journal Classification (ASJC) codes

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