Abstract
Most of the traditional methods for shape classification are based on contour. They often encounter difficulties when dealing with classes that have large nonlinear variability, especially when the variability is structural or due to articulation. It is well-known that shape representation based on skeletons is superior to contour based representation in such situations. However, approaches to shape similarity based on skeletons suffer from the instability of skeletons and matching of skeleton graphs is still an open problem. Using a skeleton pruning method, we are able to obtain stable pruned skeletons even in the presence of significant contour distortions. In contrast to most existing methods, it does not require converting of skeleton graphs to trees and it does not require any graph editing. We represent each shape as set of shortest paths in the skeleton between pairs of skeleton endpoints. Shape classification is done with Bayesian classifier. We present excellent classification results for complete shape.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Trans. PAMI 24(4), 509–522 (2002)
Sebastian, T., Klein, P., Kimia, B.: Shock-Based Indexing into Large Shape Databases. In: Tistarelli, M., Bigun, J., Jain, A.K. (eds.) ECCV 2002. LNCS, vol. 2359, pp. 731–746. Springer, Heidelberg (2002)
Sun, K.B., Super, B.J.: Classification of Contour Shapes Using Class Segment Sets. CVPR, 727–733 (2005)
Blum, H.: Biological Shape and Visual Science. J. Theoretical Biology 38, 205–287 (1973)
Sebastian, T.B., Kimia, B.B.: Curves vs skeletons in object recognition. Signal Processing 85, 247–263 (2005)
Basri, R., Costa, L., Geiger, D., Jacobs, D.: Determining the Similarity of Deformable Shapes. Vision Research 38, 2365–2385 (1998)
Huttenlocher, D.P., Klanderman, G.A., Rucklidge, W.J.: Comparing Images Using the Hausdorff Distance. IEEE Trans. Pattern Analysis and Machine Intelligence 15(9), 850–863 (1993)
Shaked, D., Bruckstein, A.M.: Pruning Medial Axes. Computer Vision and Image Understanding 69(2), 156–169 (1998)
Choi, W.-P., Lam, K.-M., Siu, W.-C.: Extraction of the Euclidean skeleton based on a connectivity criterion. Pattern Recognition 36(3), 721–729 (2003)
Bai, X., Latecki, L.J., Liu, W.-Y.: Skeleton Pruning by Contour Partitioning with Discrete Curve Evolution. IEEE Trans. Pattern Analysis and Machine Intelligence 29(3), 449–462 (2007)
Ogniewicz, R.L., Kubler, O.: Hierarchic voronoi skeletons. Pattern Recognition 28, 343–359 (1995)
Zhu, S.C., Yuille, A.L.: FORMS: A flexible object recognition and modeling system. Int. J. Computer Vision (IJCV) 20(3), 187–212 (1996)
Liu, T., Geiger, D.: Approximate Tree Matching and Shape Similarity. In: Proc. Int. Conf. Computer Vision (ICCV), pp. 456–462 (1999)
Geiger, D., Liu, T., Kohn, R.V.: Representation and self-similarity of shapes. IEEE Trans. Pattern Anal. Mach. Intell. 25(1) (2003)
Di Ruberto, C.: Recognition of shapes by attributed skeletal graphs. Pattern Recognition 37(1), 21–31 (2004)
Pelillo, M., Siddiqi, K., Zucker, S.W.: Matching hierarchical structures using association graphs. IEEE Trans. Pattern Anal. Mach. Intell. 21(11), 1105–1120 (1999)
BarHill, A., Hertz, T., Weinshall, D.: Object class recognition by boosting a part based model. CVPR (2005)
Fussenegger, M., Opelt, A., Pinz, A., Auer, P.: Object Recognition Using Segmentation for Feature Detection. In: Proc. Int. Conf. Pattern Recognition (ICPR), pp. 41–44 (2004)
Gorelick, L., Galun, M., Sharon, E., Basri, R., Brandt, A.: Shape Representation and Classification Using the Poisson Equation. IEEE Trans. Pattern Analysis and Machine Intelligence 28(12), 1991–2005 (2006)
Aslan, C., Tari, S.: An Axis Based Representation for Recognition. ICCV, 1339–1346 (2005)
Torsello, A., Hancock, E.R.: A skeletal measure of 2D shape similarity. Computer Vision and Image Understanding 95(1), 1–29 (2004)
Shokoufandeh, A., Macrini, D., Dickinson, S., Siddiqi, K., Zucker, S.W.: Indexing hierarchical structures using graph spectra. IEEE Trans. Pattern Analysis and Machine Intelligence 27(7), 1125–1140 (2005)
Sebastian, T.B., Klein, P.N., Kimia, B.B.: Recognition of shapes by editing their shock graphs. IEEE Trans. Pattern Analysis and Machine Intelligence 26(5), 550–571 (2004)
Siddiqi, K., Shokoufandeh, A., Dickenson, S.J., Zucker, S.W.: Shock graphs and shape matching. ICCV, 222–229 (1998)
Siddiqi, K., Shokoufandeh, A., Dickinson, S., Zucker, S.: Shock graphs and shape matching. International Journal of Computer Vision 30, 1–24 (1999)
Tu, Z., Yuille, A.L.: Shape Matching and Recognition Using Generative Models and Informative Features. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3024, pp. 195–209. Springer, Heidelberg (2004)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yang, X., Bai, X., Yu, D., Latecki, L.J. (2007). Shape Classification Based on Skeleton Path Similarity. In: Yuille, A.L., Zhu, SC., Cremers, D., Wang, Y. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2007. Lecture Notes in Computer Science, vol 4679. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74198-5_29
Download citation
DOI: https://doi.org/10.1007/978-3-540-74198-5_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74195-4
Online ISBN: 978-3-540-74198-5
eBook Packages: Computer ScienceComputer Science (R0)