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3D Model Retrieval Using Probability Density-Based Shape Descriptors
June 2009 (vol. 31 no. 6)
pp. 1117-1133
Ceyhun Burak Akgül, Philips Research Europe, High Tech Campus, The Netherlands
Bülent Sankur, Boǧaziçi University, Istanbul
Yücel Yemez, Koç University, Istanbul
Francis Schmitt, Télécom ParisTech, Paris
We address content-based retrieval of complete 3D object models by a probabilistic generative description of local shape properties. The proposed shape description framework characterizes a 3D object with sampled multivariate probability density functions of its local surface features. This density-based descriptor can be efficiently computed via kernel density estimation (KDE) coupled with fast Gauss transform. The non-parametric KDE technique allows reliable characterization of a diverse set of shapes and yields descriptors which remain relatively insensitive to small shape perturbations and mesh resolution. Density-based characterization also induces a permutation property which can be used to guarantee invariance at the shape matching stage. As proven by extensive retrieval experiments on several 3D databases, our framework provides state-of-the-art discrimination over a broad and heterogeneous set of shape categories.

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Index Terms:
Shape, Nonparametric statistics, Retrieval models, Curve, surface, solid, and object representations, Feature representation, Invariants, Feature evaluation and selection
Citation:
Ceyhun Burak Akgül, Bülent Sankur, Yücel Yemez, Francis Schmitt, "3D Model Retrieval Using Probability Density-Based Shape Descriptors," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 6, pp. 1117-1133, June 2009, doi:10.1109/TPAMI.2009.25
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