Abstract
Optimal Subsequence Bijection (OSB) is a method that allows comparing two sequences of endnodes of two skeleton graphs which represent articulated shapes of 2D images. The OSB dissimilarity function uses a constant penalty cost for all endnodes not matching between two skeleton graphs; this can be a problem, especially in those cases where there is a big amount of not matching endnodes. In this paper, a new penalty scheme for OSB, assigning variable penalties on endnodes not matching between two skeleton graphs, is proposed. The experimental results show that the new penalty scheme improves the results on supervised classification, compared with the original OSB.
Chapter PDF
Similar content being viewed by others
References
Sebastian, T.B., Kimia, B.B.: Curves vs Skeleton in Object Recognition. Signal Processing 85(2), 247–263 (2005)
Basri, R., Costa, L., Geiger, D., Jacobs, D.: Determinig the Similarity of Deformable Shapes. Vision Research 38, 2365–2385 (1998)
Huttenlocher, D.P., Klandeman, G.A., Rucklidge, W.J.: Comparing Images Using the Hausdorff Distance. IEEE Transaction Pattern Analysis and Machine Intelligence 15(9), 850–863 (1993)
Belongie, S., Puzhicha, J., Malik, J.: Shape Matching and Object Recognition Using Shape Contexts. IEEE Transaction Pattern Analysis and Machine Intelligence 24(4), 509–522 (2002)
Bai, X., Latecki, L.: Path Similarity Skeleton Graph Matching. IEEE Transactions on Pattern Analysis and Machine Intelligence 30, 1282–1292 (2008)
Shen, W., Wang, Y., Bai, X., Wang, L., Latecki, L.: Shape Clustering Common Structure Discovery. Pattern Recognition 64, 539–550 (2013)
Zhu, S.C., Yuille, A.L.: FORMS: A Flexible Object Recognition and Modeling Sys-tem. Proceedings of International Journal of Computer Vision 20(3), 187–212 (1996)
Liu, T., Geiger, D.: Approximate Tree Matching and Shape Similarity. In: Proceedings of IEEE 7th International Conference on Computer Vision, pp. 456–462 (1999)
Siddiqi, K., Shkoufandeh, A., Zucker, S.: Shock Graphs and Shape Matching. Proceedings of International Journal of Computer Vision 35, 13–32 (1998)
Shokoufandeh, A., Macrini, D., Dickinson, S., Siddiqi, K., Zucker, S.W.: Indexing Hierarchical Structures Using Graphs Spectra. IEEE Trans. Pattern Analysis and Machine Intelligence 27, 1125–1140 (2005)
Sebastian, T.B., Klein, P., Kimia, B.B.: Recognition of Shapes by Editing Shocks Graphs. In: Proceedings of International Conference in Computer Vision, pp. 755–762 (2001)
Torsello, A., Hancock, E.R.: A Skeletal Measure of 2D Shape Similarity. Computer Vision and Image Understanding 95, 1–29 (2004)
Bai, X., Liu, W., Tu, Z.: Integrating contour and skeleton for shape classification. In: IEEE 12th International Conference on Computer Vision Workshops (2009)
Bai, X., Latecki, L.J.: Discrete Skeleton Evolution. In: Yuille, A.L., Zhu, S.-C., Cremers, D., Wang, Y. (eds.) EMMCVPR 2007. LNCS, vol. 4679, pp. 362–374. Springer, Heidelberg (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Pinilla-Buitrago, L.A., Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A. (2013). New Penalty Scheme for Optimal Subsequence Bijection . In: Ruiz-Shulcloper, J., Sanniti di Baja, G. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2013. Lecture Notes in Computer Science, vol 8258. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41822-8_26
Download citation
DOI: https://doi.org/10.1007/978-3-642-41822-8_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41821-1
Online ISBN: 978-3-642-41822-8
eBook Packages: Computer ScienceComputer Science (R0)