Abstract
In this paper we describe our 3D object signature for 3D object classification. The signature is based on a learning approach that finds salient points on a 3D object and represent these points in a 2D spatial map based on a longitude-latitude transformation. Experimental results show high classification rates on both pose-normalized and rotated objects and include a study on classification accuracy as a function of number of rotations in the training set.
Chapter PDF
Similar content being viewed by others
References
Assfalg, J., Bimbo, A.D., Pala, P.: Content-based retrieval of 3d models through curvature maps: a cbr approach exploiting media conversion. Multimedia Tools Appl. 31, 29–50 (2006)
Atmosukarto, I., Travillian, R., Franklin, J., Shapiro, L., Brinkley, J., Suciu, D.: A unifiying framework for combining content-based image retrieval with relational database queries for biomedical applications, SIIM (2008)
Biasotti, S., Giorgi, D., Marini, S., Spagnuolo, M., Facidieno, B.: A Comparison Framework for 3D Object Classification Methods. MRCS, 314–321 (2006)
Huber, D., Kapuria, A., Donamukkala, R.R., Hebert, M.: Parts-based 3D object classification. In: Proc. IEEE CVPR (2004)
Autodesk 3d studio max, http://usa.autodesk.com
Correa, S.R., Shapiro, L., Meila, M., Berson, G., Cunnnigham, M., Sze, M.: Symbolic Signatures for Deformable Shapes. IEEE Trans. PAMI 28(1), 75–90 (2004)
Kadir, T., Brady, M.: Scale, Saliency and Image Description. IJCV 45(2), 83–105 (2001)
Lowe, D.: Distinctive image features from scale-invariant keypoints. IJCV 60(2), 91–110 (2004)
Lee, C.H., Varshney, A., Jacobs, D.W.: Mesh saliency. ACM Trans. Graph 24(3), 659–666 (2005)
Novatnack, J., Nishino, K.: Scale-dependent 3d geometric features. In: Proc. ICCV (2007)
Ohbuchi, R., Osada, K., Furuya, T., Banno, T.: Salient local visual features for shape-based 3d model retrieval. In: Proc. IEEE SMI (2008)
Scholkopf, B., Smola, A.J.: Learning with kernels. Cambridge Uni. Press, Cambridge (2002)
Vapnik, V.V.: Statistical Learning Theory. John Wiley and Sons, Chichester (1998)
Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Atmosukarto, I., Shapiro, L.G. (2008). A Learning Approach to 3D Object Representation for Classification. In: da Vitoria Lobo, N., et al. Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2008. Lecture Notes in Computer Science, vol 5342. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89689-0_31
Download citation
DOI: https://doi.org/10.1007/978-3-540-89689-0_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-89688-3
Online ISBN: 978-3-540-89689-0
eBook Packages: Computer ScienceComputer Science (R0)