Issue No. 06 - June (2009 vol. 31)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPAMI.2009.25
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.
Shape, Nonparametric statistics, Retrieval models, Curve, surface, solid, and object representations, Feature representation, Invariants, Feature evaluation and selection
B. Sankur, C. B. Akgül, F. Schmitt and Y. Yemez, "3D Model Retrieval Using Probability Density-Based Shape Descriptors," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 31, no. , pp. 1117-1133, 2009.