A spectral graph wavelet approach for nonrigid 3D shape retrieval

作者：Masoumi Majid; Li Chunyuan; Ben Hamza A^*

来源：Pattern Recognition Letters, 2016, 83: 339-348.

DOI：10.1016/j.patrec.2016.04.009

• Shape retrieval
• Spectral graph wavelet
• Geodesic kernel
• Bag-of-features

In this paper, we propose a spectral graph wavelet approach for 3D shape retrieval using the bag-of-features paradigm. In an effort to capture both local and global characteristics of a 3D shape, we present a three-step feature description framework. Local descriptors are first extracted via the spectral graph wavelet transform having the Mexican hat wavelet as a generating kernel. Then, mid-level features are obtained by embedding local descriptors into the visual vocabulary space using the soft-assignment coding step of the bag-of-features model. A global descriptor is subsequently constructed by aggregating mid-level features weighted by a geodesic exponential kernel, resulting in a matrix representation that describes the frequency of appearance of nearby codewords in the vocabulary. Then, we compare the global descriptor of a query to all global descriptors of the shapes in the dataset using a dissimilarity measure and find the closest shape. Experimental results on two standard 3D shape benchmarks demonstrate the effectiveness of the proposed shape retrieval approach in comparison with state-of-the-art methods.

• 出版日期2016-11-1