Towards Automatic Classification of 3-D Museum Artifacts Using Ontological Concepts

The development and use of content-based retrieval techniques for 3-D models is a relatively new departure in multimedia retrieval. We have extended our existing multimedia museum information system to support content-, metadata- and concept-based retrieval of 3-D models of museum artifacts and in this paper we describe a “classifier agent” to automatically assign associations between 3-D artifacts and concepts and metadata stored in a domain ontology. The context of the classifier agent is described, together with an overview of its architecture. Selecting appropriate parameters for the agent is an important activity and a comparison is made between manually selected parameters and the results of an automatic technique to determine “optimal” settings.

[1]  Francoise J. Preteux,et al.  Hough transform-based 3D mesh retrieval , 2001, SPIE Optics + Photonics.

[2]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[3]  Berthold K. P. Horn.  Extended Gaussian images , 1984, Proceedings of the IEEE.

[4]  Remco C. Veltkamp,et al.  A Survey of Content Based 3D Shape Retrieval Methods , 2004, SMI.

[5]  Kirk Martinez,et al.  3-D shape descriptors and distance metrics for content-based artifact retrieval , 2005, IS&T/SPIE Electronic Imaging.

[6]  Tony Tung,et al.  Augmented Reeb graphs for content-based retrieval of 3D mesh models , 2004, Proceedings Shape Modeling Applications, 2004..

[7]  David Beasley,et al.  An overview of genetic algorithms: Part 1 , 1993 .

[8]  Paul H. Lewis,et al.  SCULPTEUR: Towards a New Paradigm for Multimedia Museum Information Handling , 2003, SEMWEB.

[9]  Hans-Peter Kriegel,et al.  Nearest Neighbor Classification in 3D Protein Databases , 1999, ISMB.

[10]  Matthew Goldstein,et al.  Kn -nearest Neighbor Classification , 1972, IEEE Trans. Inf. Theory.

[11]  Russell C. Eberhart,et al.  A discrete binary version of the particle swarm algorithm , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[12]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[13]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[14]  Bernard Chazelle,et al.  Matching 3D models with shape distributions , 2001, Proceedings International Conference on Shape Modeling and Applications.

[15]  John Mylopoulos,et al.  The Semantic Web - ISWC 2003 , 2003, Lecture Notes in Computer Science.

[16]  C. Goose,et al.  Glossary of Terms , 2004, Machine Learning.

[17]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[18]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[19]  Paul H. Lewis,et al.  SCULPTEUR: Multimedia Retrieval for Museums , 2004, CIVR.

[20]  Marc Rioux,et al.  Nefertiti: a query by content system for three-dimensional model and image databases management , 1999, Image Vis. Comput..

[21]  Wei-Ying Ma,et al.  Image and Video Retrieval , 2003, Lecture Notes in Computer Science.

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

[23]  Bernt Schiele,et al.  3D object recognition from range images using local feature histograms , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.