SHREC’08 entry: Semi-supervised learning for semantic 3D model retrieval

A shape feature by itself is not sufficient for effective 3D model retrieval. Long-lasting semantics shared by a community as well as a short-lived intention of a user determines the similarity of 3D models. In this paper, we describe a method of shape-based 3D model retrieval that employs off-line, semi-supervised learning of multiple classes in the database to capture long-lasting, shared semantic knowledge. The method performs two learning based dimension reductions, first one to accommodate distribution of features in the feature space and the second one to accommodate the semantic knowledge embodied in a set of user-defined semantic labels. We evaluate the method by using the SHREC'08 3D generic and CAD models track.

[1]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[2]  Eric Wahl,et al.  Surflet-pair-relation histograms: a statistical 3D-shape representation for rapid classification , 2003, Fourth International Conference on 3-D Digital Imaging and Modeling, 2003. 3DIM 2003. Proceedings..

[3]  Thomas A. Funkhouser,et al.  The Princeton Shape Benchmark , 2004, Proceedings Shape Modeling Applications, 2004..

[4]  Xiaofei He,et al.  Locality Preserving Projections , 2003, NIPS.

[5]  Akihiro Yamamoto,et al.  Learning semantic categories for 3D model retrieval , 2007, MIR '07.

[6]  Ryutarou Ohbuchi,et al.  Shape similarity comparison of 3D models using alpha shapes , 2003, 11th Pacific Conference onComputer Graphics and Applications, 2003. Proceedings..