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

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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 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 re...
Michael Bronstein, Iasonas Kokkinos
Added 30 Mar 2010
Updated 14 May 2010
Type Conference
Year 2010
Where CVPR
Authors Michael Bronstein, Iasonas Kokkinos
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