Abstract
3D shape classification plays an important role in the process of organizing and retrieving models in large databases. Classifying shapes means to assign a query model to the most appropriate class of objects: knowledge about the membership of models to classes can be very useful to speed up and improve the shape retrieval process, by allowing the reduction of the candidate models to compare with the query.
The main contribution of this paper is the setting of a framework to compare the effectiveness of different query-to-class membership measures, defined independently of specific shape descriptors. The classification performances are evaluated against a set of popular 3D shape descriptors, using a dataset consisting of 14 classes made up of 20 objects each.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Tangelder, J., Veltkamp, R.: A survey of content based 3d shape retrieval methods. In: Proc. Shape Modeling Applications 2004, pp. 145–156 (2004)
Bustos, B., Keim, D.A., Saupe, D., Schreck, T., Vranić, D.V.: Feature-based similarity search in 3D object databases. ACM Computing Surveys 37(4), 345–387 (2005)
Sengupta, K., Boyer, K.L.: Organizing large structural mordelbases. IEEE Trans. on Pattern Analysis and Machine Intelligence 17(4), 321–332 (1995)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. John Wiley and Sons Inc., Chichester (2001)
Lam, W., Keung, C.K., Liu, D.: Discovering useful concept prototypes for classification based on filtering and abstraction. IEEE Trans. on Pattern Analysis and Machine Intelligence 24(8), 1075–1090 (2002)
Donamukkala, R., Huber, D., Kapuria, A., Hebert, M.: Automatic class selection and prototyping of 3-D object classification. In: Proc. 5th Int. Conf. on 3-D Digital Imaging and Modeling /3DIM 2005, pp. 64–71. IEEE, Los Alamitos (2005)
Csákáky, P., Wallace, A.M.: Representation and classification of 3-D objects. IEEE Trans. on Systems, Man and Cybernetics - Part B: Cybern. 33(4), 638–647 (2003)
Huber, D., Kapuria, A., Donamukkala, R., Hebert, M.: Part-based 3D object classification. In: Proc. IEEE Conf. on Computer Vision and pattern Recognition (CVPR 2004), vol. 2, pp. 82–89 (2004)
Zhang, J.: Selecting typical instances in instance-based learning. In: Proc. Int. conf. Machine Learning, pp. 470–479 (1992)
Kazhdan, M., Funkhouser, T., Rusinkiewicz, S.: Rotation invariant spherical harmonic representation of 3D shape descriptors. In: Kobbelt, L., Schröder, P., Hoppe, H., (eds.) Proc. Symposium in Geometry Processing, pp. 156–165 (2003)
Chen, D., Ouhyoung, M., Tian, X., Shen, Y.: On visual similarity based 3D model retrieval. Computer Graphics Forum 22, 223–232 (2003)
Hilaga, M., Shinagawa, Y., Kohmura, T., Kunii, T.L.: Topology matching for fully automatic similarity estimation of 3D shapes. In: Computer Graphics Proceedings, Annual Conference Series: SIGGRAPH Conference, pp. 203–212 (2001)
Biasotti, S., Marini, S.: 3D object comparison based on shape descriptors. International Journal of Computer Applications in Technology 23(2/3/4), 57–69 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Biasotti, S., Giorgi, D., Marini, S., Spagnuolo, M., Falcidieno, B. (2006). A Comparison Framework for 3D Object Classification Methods. In: Gunsel, B., Jain, A.K., Tekalp, A.M., Sankur, B. (eds) Multimedia Content Representation, Classification and Security. MRCS 2006. Lecture Notes in Computer Science, vol 4105. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11848035_42
Download citation
DOI: https://doi.org/10.1007/11848035_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-39392-4
Online ISBN: 978-3-540-39393-1
eBook Packages: Computer ScienceComputer Science (R0)