Title: Shape Classification using Spectral Graph Wavelets,
Abstract: Spectral shape descriptors have been used extensively in a broad spectrum of
geometry processing applications ranging from shape retrieval and segmentation
to classification. In this pa- per, we propose a spectral graph wavelet
approach for 3D shape classification using the bag-of-features paradigm. In an
effort to capture both the local and global geometry of a 3D shape, we present
a three-step feature description framework. First, local descriptors are
extracted via the spectral graph wavelet transform having the Mexican hat
wavelet as a generating ker- nel. Second, mid-level features are obtained by
embedding lo- cal descriptors into the visual vocabulary space using the soft-
assignment coding step of the bag-of-features model. Third, a global descriptor
is constructed by aggregating mid-level fea- tures weighted by a geodesic
exponential kernel, resulting in a matrix representation that describes the
frequency of appearance of nearby codewords in the vocabulary. Experimental
results on two standard 3D shape benchmarks demonstrate the effective- ness of
the proposed classification approach in comparison with state-of-the-art
methods.