A spectral graph wavelet approach for nonrigid 3D shape retrieval
We propose a spectral graph wavelet approach for 3D shape retrieval using the bag of features paradigm.We present a three step feature description framework to capture both local and global characteristics of shapes.We construct a global descriptor by aggregating mid level features weighted by a geodesic exponential kernel.We demonstrate the better performance of our approach using various evaluation metrics.We conducted a comprehensive comparison with recent state of the art algorithms. 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.
Pattern Recognit. Lett.