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

作者：Michael M. Bronstein, Iasonas Kokkinos · 年份：2010 · DOI：10.1109/cvpr.2010.5539838 · 被引用次数：701 · 研究领域：Image Retrieval and Classification Techniques、Advanced Image and Video Retrieval Techniques、Image Processing and 3D Reconstruction

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.