Advertisement

Shape Skeleton Classification Using Graph and Multi-scale Fractal Dimension

  • André R. Backes
  • Odemir M. Bruno
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6134)

Abstract

This paper presents a novel approach to shape characterization, where a shape skeleton is modelled as a dynamic graph, and its complexity is evaluated in a dynamic evolution context. Descriptors achieved by using this approach show to be efficient in the characterization of different shape patterns with different variations in their structure (such as, occlusion, articulation and missing parts). Experiments using a generic set of shapes are presented as also a comparison with traditional shape analysis methods, such as Fourier descriptors, Curvature, Zernike moments and Bouligand-Minkowski. Although the reduced amount of information present in the shape skeleton, results show that the method is efficient for shape characterization tasks, overcoming the traditional approaches.

Keywords

graph shape analysis skeleton multi-scale fractal dimension 

References

  1. 1.
    Blum, H.: A transformation for extracting new descriptors of shape. In: Wathen-Dunn, W. (ed.) Models for the Perception of Speech and Visual Forms, pp. 362–380. MIT Press, Amsterdam (1967)Google Scholar
  2. 2.
    Blum, H., Nagel, R.: Shape description using weighted symmetric axis features. Pattern Recognition 10(3), 167–180 (1978)zbMATHCrossRefGoogle Scholar
  3. 3.
    da S. Torres, R., Falcão, A., da F. Costa, L.: A graph-based approach for multiscale shape analysis. Pattern Recognition 37, 1163–1174 (2003)Google Scholar
  4. 4.
    Wang, C., Cannon, D.J., Kumara, S.R.T., Guowen, L.: A skeleton and neural network-based approach for identifying cosmetic surface flaws. IEEE Transactions on Neural Networks 6(5), 1201–1211 (1995)CrossRefGoogle Scholar
  5. 5.
    Zhu, X.: Shape recognition based on skeleton and support vector machines. In: Third International Conference on Intelligent Computing, pp. 1035–1043 (2007)Google Scholar
  6. 6.
    Backes, A.R., Bruno, O.M.: Shape classification using complex network and multi-scale fractal dimension. Pattern Recognition Letters 31(1), 44–51 (2010)CrossRefGoogle Scholar
  7. 7.
    Boccaletti, S., Latora, V., Moreno, Y., Chavez, M., Hwang, D.U.: Complex networks: Structure and dynamics. Physics Reports 424(4-5), 175–308 (2006)CrossRefMathSciNetGoogle Scholar
  8. 8.
    da, F., Costa, L., Rodrigues, F.A., Travieso, G., Villas Boas, P.R.: Characterization of complex networks: A survey of measurements. Advances in Physics 56(1), 167–242 (2007)CrossRefGoogle Scholar
  9. 9.
    Mandelbrot, B.: The fractal geometry of nature. Freeman & Co., New York (2000)Google Scholar
  10. 10.
    Tricot, C.: Curves and Fractal Dimension. Springer, Heidelberg (1995)zbMATHGoogle Scholar
  11. 11.
    Schroeder, M.: Fractals, Chaos, Power Laws: Minutes From an Infinite Paradise. W. H. Freeman, New York (1996)Google Scholar
  12. 12.
    Kim, J.S., Goh, K.I., Salvi, G., OH, E., Kahng, B., Kim, D.: Fractality in complex networks: critical and supercritical skeletons. Physical Review E 75, 16110 (2007)CrossRefGoogle Scholar
  13. 13.
    Barabási, A.L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)CrossRefMathSciNetGoogle Scholar
  14. 14.
    Feder, J.: Fractals. Plenum, New York (1988)zbMATHGoogle Scholar
  15. 15.
    Emerson, C.W., Lam, N.N., Quattrochi, D.A.: Multi-scale fractal analysis of image texture and patterns. Photogrammetric Engineering and Remote Sensing 65(1), 51–62 (1999)Google Scholar
  16. 16.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Prentic-Hall, New Jersey (2002)Google Scholar
  17. 17.
    Plotze, R.O., Padua, J.G., Falvo, M., Vieira, M.L.C., Oliveira, G.C.X., Bruno, O.M.: Leaf shape analysis by the multiscale minkowski fractal dimension, a new morphometric method: a study in passiflora (passifloraceae). Canadian Journal of Botany-Revue Canadienne de Botanique 83, 287–301 (2005)CrossRefGoogle Scholar
  18. 18.
    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)CrossRefGoogle Scholar
  19. 19.
    Sharvit, D., Chan, J., Tek, H., Kimia, B.B.: Symmetry-based indexing of image databases. Journal of Visual Communication and Image Representation 9(4), 366–380 (1998)CrossRefGoogle Scholar
  20. 20.
    Osowski, S., Nghia, D.D.: Fourier and wavelet descriptors for shape recognition using neural networks - a comparative study. Pattern Recognition 35(9), 1949–1957 (2002)zbMATHCrossRefGoogle Scholar
  21. 21.
    Zhenjiang, M.: Zernike moment-based image shape analysis and its application. Pattern Recognition Letters 21(2), 169–177 (2000)CrossRefGoogle Scholar
  22. 22.
    Wu, W.Y., Wang, M.J.: On detecting the dominant points by the curvature-based polygonal approximation. CVGIP: Graphical Models Image Process 55, 79–88 (1993)CrossRefGoogle Scholar
  23. 23.
    Everitt, B.S., Dunn, G.: Applied Multivariate Analysis, 2nd edn. Arnold (2001)Google Scholar
  24. 24.
    Fukunaga, K.: Introduction to Statistical Pattern Recognition, 2nd edn. Academic Press, London (1990)zbMATHGoogle Scholar
  25. 25.
    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)CrossRefGoogle Scholar
  26. 26.
    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)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • André R. Backes
    • 1
  • Odemir M. Bruno
    • 2
  1. 1.Instituto de Ciências Matemáticas e de Computação (ICMC)Universidade de São Paulo (USP)São CarlosBrazil
  2. 2.Instituto de Física de São Carlos (IFSC)Universidade de São Paulo (USP)São CarlosBrazil

Personalised recommendations