Skip to main content
SpringerLink
Log in

Shape Matching Based on Skeletonization and Alignment of Primitive Chains

  • Conference paper
  • First Online:
Analysis of Images, Social Networks and Texts (AIST 2015)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 542))

Abstract

We introduce a new shape matching approach based on skeletonization and alignment of primitive chains. At the first stage the skeleton of a binary image is traversed counterclockwise in order to encode it by chain of primitives. A primitive describes topological properties of the correlated edge and consists of a pair of numbers: the length of some edge and the angle between this and the next edges. We offer to expand a primitive by the information about the radial function of the skeleton rib. To get the compact width description we interpolate radial function by Legendre polynomials and find the vector of Legendre coefficients. Thus the resulting shape representation by the chain of primitives includes not only topological properties but also the contour ones. Then we suggest the dynamic programming procedure of the alignment of two primitive chains in order to match correspondent shapes. Based on the optimal alignment we propose the pair-wise dissimilarity function which is evaluated on artificial image dataset and the Flavia leaf dataset.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Attali, D., Sanniti di Baja, G., Thiel, E.: Skeleton simplification through non significant branch removal. Image Process. Commun. 3(3–4), 63–72 (1997)

    Google Scholar 

  2. Bai, X., Latecki, L.J.: Path similarity skeleton graph matching. IEEE Trans. Pattern Anal. Mach. Intell. 30(7), 1282–1292 (2008)

    Article  Google Scholar 

  3. Balfer, J., Schöler, F., Steinhage V.: Semantic Skeletonization for Structural Plant Analysis. Submitted to International Conference on Functional-Structural Plant Models (2013)

    Google Scholar 

  4. Beghin, T., Cope, J.S., Remagnino, P., Barman, S.: Shape and texture based plant leaf classification. In: Blanc-Talon, J., Bone, D., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2010, Part II. LNCS, vol. 6475, pp. 345–353. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  5. Blum, H.: A transformation for extracting new descriptors of shape. Models Percept. Speech Vis. Form 19(5), 362–380 (1967)

    Google Scholar 

  6. Bystrov, M.Y.: Structural approach application for recognition of binary image skeleton. Proc. Petrozavodsk State Univ. 2(115), 76–80 (2011). (in Russian)

    Google Scholar 

  7. Demirci, M.F., Shokoufandeh, A., Keselman, Y., Bretzner, L., Dickinson, S.: Object recognition as many-to-many feature matching. Int. J. Comput. Vision 69(2), 203–222 (2006)

    Article  MATH  Google Scholar 

  8. Du, J.X., Huang, D.S., Wang, X.F., Gu, X.: Computer-aided plant species identification (CAPSI) based on leaf shape matching technique. Trans. Inst. Measur. Control 28(3), 275–285 (2006)

    Article  Google Scholar 

  9. ImageCLEF, Plant Identification (2012). http://imageclef.org/2012/plant

  10. Intelligent Computing Laboratory, Chinese Academy of Sciences Homepage. http://www.intelengine.cn/English/dataset

  11. Jänichen, S., Perner, P.: Aligning concave and convex shapes. In: Yeung, D.-Y., Kwok, J.T., Fred, A., Roli, F., de Ridder, D. (eds.) SSPR 2006 and SPR 2006. LNCS, vol. 4109, pp. 243–251. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  12. Kadir, A., Nugroho, L.E., Susanto, A., Santosa, P.I.: A comparative experiment of several shape methods in recognizing plants (2011). arXiv preprint arXiv:1110.1509

  13. Klein, P., Tirthapura, S., Sharvit, D., Kimia, B.: A tree-edit-distance algorithm for comparing simple, closed shapes. In: Proceedings of the eleventh annual ACM-SIAM Symposium on Discrete Algorithms. Society for Industrial and Applied Mathematics, pp. 696–704 (2000)

    Google Scholar 

  14. Kushnir, O., Seredin, O.: Parametric description of skeleton radial function by legendre polynomials for binary images comparison. In: Elmoataz, A., Lezoray, O., Nouboud, F., Mammass, D. (eds.) ICISP 2014. LNCS, vol. 8509, pp. 520–530. Springer, Heidelberg (2014)

    Google Scholar 

  15. Lam, L., Lee, S.-W., Suen, C.Y.: Thinning methodologies - a comprehensive survey. IEEE Trans. Pattern Anal. Mach. Intell. 14(9), 869–885 (1992)

    Article  Google Scholar 

  16. LEAF - Tree Leaf Database, Inst. of Information Theory and Automation ASCR, Prague, Czech Republic. http://zoi.utia.cas.cz/tree_leaves

  17. Lee, D.: Medial axis transformation of a planar shape. IEEE Trans. Pat. Anal. Mach. Int. PAMI 4(4), 363–369 (1982)

    Article  MATH  Google Scholar 

  18. Mallah, C., Cope, J., Orwell, J.: Plant leaf classification using probabilistic integration of shape, texture and margin features. Computer Graphics and Imaging/798: Signal Processing, Pattern Recognition and Applications (CGIM2013), Acta Press (2013). doi:10.2316/P.2013.798-098

  19. Maragos, P.: Pattern spectrum and multiscale shape representation. IEEE Trans. Pattern Anal. Mach. Intell. 11(7), 701–716 (1989)

    Article  MATH  Google Scholar 

  20. Mestetskiy, L., Semenov, A.: Binary image skeleton - continuous approach. VISAPP 1, 251–258 (2008)

    Google Scholar 

  21. Mottl, V.V., Blinov, A.B., Kopylov, A.V., Kostin, A.A.: Optimization techniques on pixel neighborhood graphs for image processing. In: Jolion, J.-M., Kropatsch, W.G. (eds.) Graph-Based Representations in Pattern Recognition. Computing Supplement, vol. 12, pp. 135–145. Springer, Wien (1998)

    Chapter  Google Scholar 

  22. Mottl, V., Seredin, O., Dvoenko, S., Kulikowski, C., Muchnik, I.: Featureless pattern recognition in an imaginary Hilbert space. In: Proceedings of 16th International Conference on Pattern Recognition, vol. 2, pp. 88–912 (2002)

    Google Scholar 

  23. Mottl, V., Krasotkina, O., Seredin, O., Muchnik, I.: Kernel fusion and feature selection in machine learning. In: Proceedings of the Eighth IASTED International Conference on Intelligent Systems and Control, Cambridge, USA, pp. 477–482 (2005)

    Google Scholar 

  24. Neuhaus, M., Bunke, H.: Edit distance-based kernel functions for structural pattern classification. Pattern Recogn. 39(10), 1852–1863 (2006)

    Article  MATH  Google Scholar 

  25. Ogniewicz, R., Kubler, O.: Hierarchic voronoi skeletons. Pattern Recogn. 28(3), 343–359 (1995)

    Article  Google Scholar 

  26. Ogniewicz, R.: Automatic medial axis pruning by mapping characteristics of boundaries evolving under the euclidean geometric heat flow onto Voronoi skeletons. Harvard Robotics Laboratory Technical report, pp. 95–114 (1995)

    Google Scholar 

  27. Reier, I.A.: Plane figure recognition based on contour homeomorphism. Pattern Recogn. Image Anal. 11(1), 242–245 (2001)

    Google Scholar 

  28. Sanniti di Baja, G., Thiel, E.: Computing and comparing distance-driven skeletons. In: Aspects of Visual Form Processing, pp. 465–486 (1994)

    Google Scholar 

  29. Sebastian, T.B., Kimia, B.: Curves vs. skeletons in object recognition. Sig. Process. 85(2), 247–263 (2005)

    Article  MATH  Google Scholar 

  30. Sederberg, T.W., Greenwood, E.: A physically based approach to 2-D shape blending. Comput. Graph. 26(2), 25–34 (1992)

    Article  Google Scholar 

  31. Serra, J.: Image Analysis and Mathematical Morphology. Acad. Press, London (1982)

    MATH  Google Scholar 

  32. Shen, W., Bai, X., Yang, X., Latecki, L.J.: Skeleton pruning as trade-off between skeleton simplicity and reconstruction error. Sci. China Inf. Sci. 56(4), 1–14 (2013)

    Article  Google Scholar 

  33. Shen, W., Wang, X., Yao, C., Bai, X.: Shape recognition by combining contour and skeleton into a mid-level representation. In: Li, S., Liu, C., Wang, Y. (eds.) CCPR 2014, Part I. CCIS, vol. 483, pp. 391–400. Springer, Heidelberg (2014)

    Google Scholar 

  34. Söderkvist, O.: Computer Vision Classification of Leaves from Swedish Trees. Diss, Linköping (2001)

    Google Scholar 

  35. Vizilter, Y.V., Sidyakin, S.V., Rubis, A.Y., Gorbatsevich, V.S.: Morphological shape comparison based on skeleton representations. Pattern Recogn. Image Anal. 22(3), 412–418 (2012)

    Article  Google Scholar 

  36. Wang, B., Brown, D., Gao, Y., La Salle, J.: MARCH: Multiscale-arch-height description for mobile retrieval of leaf images. Information Sciences (2014). http://dx.doi.org/10.1016/j.ins.2014.07.028

  37. Wang, C., Gui, C.-P., Liu, H.-K., Zhang, D., Mosig, A.: An image skeletonization based tool for pollen tube morphology analysis and phenotyping. J. Integr. Plant Biol. 55(2), 131–141 (2013)

    Article  Google Scholar 

  38. Wang, Z., Chi, Z., Feng, D.: Shape based leaf image retrieval. Vis. Image Sig. Process. IEE Proc. 150(1), 34–43 (2003)

    Article  Google Scholar 

  39. Wu, S.G., Bao, F.S., Xu, E.Y., Wang, Y.-X., Chang, Y.-F., Xiang, Q.-L.: A leaf recognition algorithm for plant classification using probabilistic neural network. In: 2007 IEEE International Symposium on Signal Processing and Information Technology, pp. 11–16 (2007)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Tula State University, Tula, Russia

    Olesia Kushnir & Oleg Seredin

Authors
  1. Olesia Kushnir
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Oleg Seredin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Olesia Kushnir .

Editor information

Editors and Affiliations

  1. Krasovsky Institute of Mathematics and Mechanics, Yekaterinburg, Russia

    Mikhail Yu. Khachay

  2. Wolverhampton, United Kingdom

    Natalia Konstantinova

  3. Technische Universität Darmstadt, Darmstadt, Germany

    Alexander Panchenko

  4. National Research University Higher School of Economics, Moscow, Russia

    Dmitry Ignatov

  5. Ural Federal University, Yekaterinbug, Russia

    Valeri G. Labunets

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Kushnir, O., Seredin, O. (2015). Shape Matching Based on Skeletonization and Alignment of Primitive Chains. In: Khachay, M., Konstantinova, N., Panchenko, A., Ignatov, D., Labunets, V. (eds) Analysis of Images, Social Networks and Texts. AIST 2015. Communications in Computer and Information Science, vol 542. Springer, Cham. https://doi.org/10.1007/978-3-319-26123-2_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-26123-2_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26122-5

  • Online ISBN: 978-3-319-26123-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation