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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. [ABSTRACT FROM AUTHOR]

Shape clustering is a difficult visual task due to large intra-class variations and small inter-class variations induced by shape articulation, rotation, occlusion, etc . To tackle this problem, we attempt to leverage the complementary nature among features of different statistics ( e.g. , skeleton-based descriptors and contour-based descriptors) for robust clustering. In this paper, a similarity fusion framework based on spectral analysis is proposed. The proposed method, which we call co-spectral, is a spectral clustering algorithm. It has two inborn merits for shape clustering: (1) it can automatically make use of the complementarity of various shape similarities based on a co-training framework; (2) it does not require shape representations to be vectors. Co-spectral is evaluated on several popular shape benchmarks. The experimental results demonstrate that co-spectral outperforms the state-of-the-art algorithms by a large margin. [ABSTRACT FROM AUTHOR]