Learning semantic categories for 3D model retrieval

A shape similarity judgment among a pair of 3D models is often influenced by their semantics, in addition to their shapes. If we could somehow incorporate semantic knowledge into a "shape similarity" comparison method, retrieval performance of a shape-based 3D model retrieval system could be improved. This paper presents a 3D model retrieval method that successfully incorporates semantic information from human-made categories (labels) in a training database. Our off-line, 2-stage semi-supervised approach learns efficiently from a small set of labeled models. The method first performs unsupervised learning from a large set of unlabeled 3D models to find a non-linear subspace on which the shape features are distributed. It then performs a supervised learning from a much smaller set of labeled 3D models to learn multiple semantic categories at once. Our experimental evaluation showed that the retrieval performance using proposed method is significantly higher than those of both supervised-only and unsupervised-only learning methods.

[1]  Wei-Ying Ma,et al.  Learning an image manifold for retrieval , 2004, MULTIMEDIA '04.

[2]  Gil-Joo Park,et al.  EVALUATION OF KERNEL BASED METHODS FOR RELEVANCE FEEDBACK IN 3D SHAPE RETRIEVAL , 2005 .

[3]  Masashi Sugiyama,et al.  Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis , 2007, J. Mach. Learn. Res..

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

[5]  Harald Niederreiter,et al.  Programs to generate Niederreiter's low-discrepancy sequences , 1994, TOMS.

[6]  Karthik Ramani,et al.  Three-dimensional shape searching: state-of-the-art review and future trends , 2005, Comput. Aided Des..

[7]  Mikhail Belkin,et al.  Laplacian Eigenmaps for Dimensionality Reduction and Data Representation , 2003, Neural Computation.

[8]  Remco C. Veltkamp,et al.  SHREC2006: 3D Shape Retrieval Contest , 2006 .

[9]  J. Tenenbaum,et al.  A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.

[10]  W TangelderJohan,et al.  A survey of content based 3D shape retrieval methods , 2008 .

[11]  Ryutarou Ohbuchi,et al.  Unsupervised learning from a corpus for shape-based 3D model retrieval , 2006, MIR '06.

[12]  Dietmar Saupe,et al.  3D Model Retrieval , 2001 .

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

[14]  Daniel A. Keim,et al.  Automatic selection and combination of descriptors for effective 3D similarity search , 2004, IEEE Sixth International Symposium on Multimedia Software Engineering.

[15]  Szymon Rusinkiewicz,et al.  Rotation Invariant Spherical Harmonic Representation of 3D Shape Descriptors , 2003, Symposium on Geometry Processing.

[16]  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..

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

[18]  Michael Elad,et al.  Content Based Retrieval of VRML Objects - An Iterative and Interactive Approach , 2001, Eurographics Multimedia Workshop.

[19]  Ryutarou Ohbuchi,et al.  Shape-similarity search of 3D models by using enhanced shape functions , 2005, Int. J. Comput. Appl. Technol..

[20]  Ron Meir,et al.  Semantic-oriented 3d shape retrieval using relevance feedback , 2005, The Visual Computer.

[22]  Ming Ouhyoung,et al.  On Visual Similarity Based 3D Model Retrieval , 2003, Comput. Graph. Forum.

[23]  Indriyati Atmosukarto,et al.  Feature Combination and Relevance Feedback for 3D Model Retrieval , 2005, 11th International Multimedia Modelling Conference.

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

[25]  Remco C. Veltkamp,et al.  A survey of content based 3D shape retrieval methods , 2004, Proceedings Shape Modeling Applications, 2004..

[26]  Matti Pietikäinen,et al.  Supervised Locally Linear Embedding , 2003, ICANN.

[27]  Akihiro Yamamoto,et al.  Comparison of Dimension Reduction Methods for Database-Adaptive 3D Model Retrieval , 2007, Adaptive Multimedia Retrieval.

[28]  Shang-Liang Chen,et al.  Orthogonal least squares learning algorithm for radial basis function networks , 1991, IEEE Trans. Neural Networks.