Abstract
A large family of shape comparison methods is based on a medial axis transform combined with an encoding of the skeleton by a graph. Despite many qualities this encoding of shapes suffers from the non continuity of the medial axis transform. In this paper, we propose to integrate robustness against structural noise inside a graph kernel. This robustness is based on a selection of the paths according to their relevance and on path editions. This kernel is positive semi-definite and several experiments prove the efficiency of our approach compared to alternative kernels.
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
Pelillo, M., Siddiqi, K., Zucker, S.: Matching hierarchical structures using association graphs. IEEE Trans. on PAMI 21(11), 1105–1120 (1999)
Sebastian, T., Klein, P., Kimia, B.: Recognition of shapes by editing their shock graphs. IEEE Trans. on PAMI 26(5), 550–571 (2004)
Goh, W.B.: Strategies for shape matching using skeletons. Computer Vision and Image Understanding 110, 326–345 (2008)
Ruberto, C.D.: Recognition of shapes by attributed skeletal graphs. Pattern Recognition 37(1), 21–31 (2004)
Siddiqi, K., Shokoufandeh, A., Dickinson, S.J., Zucker, S.W.: Shock graphs and shape matching. Int. J. Comput. Vision 35(1), 13–32 (1999)
Leymarie, F.F., Kimia, B.B.: The shock scaffold for representing 3d shape. In: Arcelli, C., Cordella, L.P., Sanniti di Baja, G. (eds.) IWVF 2001. LNCS, vol. 2059, pp. 216–229. Springer, Heidelberg (2001)
Bai, X., Latecki, J.: Path Similarity Skeleton Graph Matching. IEEE PAMI 30(7) (2008)
Vishwanathan, S., Borgwardt, K.M., Kondor, I.R., Schraudolph, N.N.: Graph kernels. Journal of Machine Learning Research 9, 1–37 (2008)
Suard, F., Rakotomamonjy, A., Bensrhair, A.: Mining shock graphs with kernels. Technical report, LITIS (2006), http://hal.archives-ouvertes.fr/hal-00121988/en/
Neuhaus, M., Bunke, H.: Edit-distance based kernel for structural pattern classification. Pattern Recognition 39, 1852–1863 (2006)
Dupé, F.X., Brun, L.: Hierarchical bag of paths for kernel based shape classification. In: SSPR 2008, pp. 227–236 (2008)
Desobry, F., Davy, M., Doncarli, C.: An online kernel change detection algorithm. IEEE Transaction on Signal Processing 53(8), 2961–2974 (2005)
Berg, C., Christensen, J.P.R., Ressel, P.: Harmonic Analysis on Semigroups. Springer, Heidelberg (1984)
Kashima, H., Tsuda, K., Inokuchi, A.: Marginalized kernel between labeled graphs. In: Proc. of the Twentieth International conference on machine Learning (2003)
DiMatteo, I., Genovese, C., Kass, R.: Bayesian curve fitting with free-knot splines. Biometrika 88, 1055–1071 (2001)
Meyer, F.: Topographic distance and watershed lines. Signal Proc. 38(1) (1994)
Torsello, A., Hancock, E.R.: A skeletal measure of 2d shape similarity. CVIU 95, 1–29 (2004)
Haussler, D.: Convolution kernels on discrete structures. Technical report, Department of Computer Science, University of California at Santa Cruz (1999)
LEMS: shapes databases, http://www.lems.brown.edu/vision/software/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Dupé, FX., Brun, L. (2009). Edition within a Graph Kernel Framework for Shape Recognition. In: Torsello, A., Escolano, F., Brun, L. (eds) Graph-Based Representations in Pattern Recognition. GbRPR 2009. Lecture Notes in Computer Science, vol 5534. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02124-4_2
Download citation
DOI: https://doi.org/10.1007/978-3-642-02124-4_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02123-7
Online ISBN: 978-3-642-02124-4
eBook Packages: Computer ScienceComputer Science (R0)