Abstract
In this work we evaluate purely structural graph measures for 3D object classification. We extract spectral features from different Reeb graph representations and successfully deal with a multi-class problem. We use an information-theoretic filter for feature selection. We show experimentally that a small change in the order of selection has a significant impact on the classification performance and we study the impact of the precision of the selection criterion. A detailed analysis of the feature participation during the selection process helps us to draw conclusions about which spectral features are most important for the classification problem.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. Journal of Machine Learning Research 3, 1157–1182 (2003)
Luo, B., Wilson, R., Hancock, E.: Spectral embedding of graphs. Pattern Recognition 36(10), 2213–2223 (2003)
Biasotti, S.: Topological coding of surfaces with boundary using Reeb graphs. Computer Graphics and Geometry 7(1), 31–45 (2005)
Escolano, F., Suau, P., Bonev, B.: Information Theory in Computer Vision and Pattern Recognition. Springer, New York (2009)
Reeb, G.: Sur les points singuliers d’une forme de Pfaff complètement intégrable ou d’une fonction numérique. Comptes Rendus 222, 847–849 (1946)
Biasotti, S., Giorgi, D., Spagnuolo, M., Falcidieno, B.: Reeb graphs for shape analysis and applications. Theoretical Computer Science 392(1–3), 5–22 (2008), doi:10.1016/j.tcs.2007.10.018.
Biasotti, S.: Computational Topology Methods for Shape Modelling Applications. PhD thesis, Universitá degli Studi di Genova (May 2004)
Hilaga, M., Shinagawa, Y., Kohmura, T., Kunii, T.L.: Topology matching for fully automatic similarity estimation of 3D shapes. In: SIGGRAPH 2001, Los Angeles, CA, pp. 203–212 (2001)
Attene, M., Biasotti, S.: Shape retrieval contest 2008: Stability of watertight models. In: SMI 2008, pp. 219–220 (2008)
Barabási, A.L., Bonabeau, E.: Scale-free networks. Scientific American 288, 50–59 (2003)
Estrada, E., Rodriguez, J.A.: Subgraph centrality in complex networks. Physical Review EÂ 71(5) (2005)
Qiu, H., Hancock, E.R.: Clustering and embedding using commute times. IEEE Transactions on PAMI 29(11), 1873–1890 (2007)
Escolano, F., Giorgi, D., Hancock, E.R., Lozano, M.A., Falcidieno, B.: Flow complexity: Fast polytopal graph complexity and 3d object clustering. In: GbRPR, pp. 253–262 (2009)
Escolano, F., Hancock, E.R., Lozano, M.A.: Birkhoff polytopes, heat kernels and graph complexity. In: ICPR, pp. 1–5 (2008)
Leonenko, N., Pronzato, L., Savani, V.: A class of rényi information estimators for multidimensional densities. The Annals of Statistics 36(5), 2153–2182 (2008)
Bonev, B., Escolano, F., Cazorla, M.: Feature selection, mutual information, and the classification of high-dimensional patterns. Pattern Analysis and Applications (February 2008)
Mount, D., Arya, S.: Ann: A library for approximate nearest neighbor searching (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bonev, B., Escolano, F., Giorgi, D., Biasotti, S. (2010). High-Dimensional Spectral Feature Selection for 3D Object Recognition Based on Reeb Graphs. In: Hancock, E.R., Wilson, R.C., Windeatt, T., Ulusoy, I., Escolano, F. (eds) Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2010. Lecture Notes in Computer Science, vol 6218. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14980-1_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-14980-1_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14979-5
Online ISBN: 978-3-642-14980-1
eBook Packages: Computer ScienceComputer Science (R0)