Parametric Shape Descriptor Based on a Scalable Boundary-Skeleton Model

  • Ivan ReyerEmail author
  • Ksenia Aminova
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 794)


A parametric shape descriptor based on the relation between contour convexities and skeleton’s branches of a shape is suggested. The descriptor contains the set of a polygonal figure convex vertices approximating a raster image and estimations of significance for curvature features corresponding to the vertices. The estimations are calculated with use of a boundary-skeleton shape models family generated by the polygonal figure. The applications of the suggested shape descriptor to the face profile line segmentation and content based image retrieval are described.


Shape analysis Boundary-skeleton shape representation Skeleton base Parametric shape descriptor 



The research was supported by the Russian Foundation for Basic Research (projects No. 14-07-00736, 17-07-01432, 17-20-02222).


  1. 1.
    Abbasi, S., Mokhtarian, F., Kittler, J.: Curvature scale space image in shape similarity retrieval. Multimedia Syst. (1999). Scholar
  2. 2.
    Achermann, B.: University of Bern face database. Copyright 1995, University of Bern, all rights reserved (1995). Scholar
  3. 3.
    Attneave, F.: Some informational aspects of visual perception. Psychol. Rev. 61(3), 183–193 (1954)CrossRefGoogle Scholar
  4. 4.
    Bartolini, I., Ciaccia, P., Patella, M.: Query processing issues in region-based image databases. Knowl. Inf. Syst. (2010). Scholar
  5. 5.
    Blum, H.: A transformation for extracting new descriptors of shape. In: Models for the Perception of Speech and Visual Form, pp. 135–143. MIT Press (1967)Google Scholar
  6. 6.
    Dudek, G., Tsotsos, J.K.: Shape representation and recognition from multiscale curvature. Comput. Vis. Image Underst. 68(2), 170–189 (1997). Scholar
  7. 7.
    Galton, A., Meathrel, R.: Qualitative outline theory. In: Proceedings of the 16th International Joint Conference on Artificial Intelligence, vol. 2, pp. 1061–1066 (1999).
  8. 8.
    Hoffman, D.D., Richards, W.A.: Parts of recognition. Cognition 18, 65–96 (1984). Scholar
  9. 9.
    Koplowitz, J., Plante, S.: Corner detection for chain coded curves. Pattern Recogn. 28(6), 843–852 (1995). Scholar
  10. 10.
    Latecki, L.J., Lakamper, R.: Shape similarity measure based on correspondence of visual parts. IEEE Trans. Pattern Anal. Mach. Intell. 22(10), 1185–1190 (2000). Scholar
  11. 11.
    Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proceedings Eighth IEEE International Conference on Computer Vision, ICCV 2001, vol. 2, pp. 416–423 (2001).
  12. 12.
    Mestetskii, L.M., Reyer, I.A.: Continuous skeletal representation of image with controllable accuracy. In: Proceedings of International Conference on Graphicon-2003, pp. 246–249 (2003, in Russian)Google Scholar
  13. 13.
    Pantic, M., Rothkrantz, L.J.M.: Facial action recognition for facial expression analysis from static face images. IEEE Trans. Syst. Man Cybern. 34, 1449–1461 (2004). Scholar
  14. 14.
    Phillips, P.J., Moon, H., Rizvi, S.A., Rauss, P.J.: The FERET evaluation methodology for face recognition algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 22, 1090–1104 (2000). Scholar
  15. 15.
    Phillips, P.J., Wechsler, H., Huang, J., Rauss, P.: The FERET database and evaluation procedure for face recognition algorithms. Image Vis. Comput. 16(5), 295–306 (1998). Scholar
  16. 16.
    Preparata, F., Shamos, M.: Computational Geometry. Springer, New York (1985)CrossRefGoogle Scholar
  17. 17.
    Ray, B.K., Pandyan, R.: ACORD - an adaptive corner detector for planar curves. Pattern Recogn. 36(3), 703–708 (2003). Scholar
  18. 18.
    Rosin, P.L.: Multiscale representation and matching of curves using codons. CVGIP: Graph. Models Image Process. 55(4), 286–310 (1993). Scholar
  19. 19.
    Zhukova, K.V., Reyer, I.A.: Parametric family of boundary-skeletal shape models. In: Proceedings of 14-th Russian Conference on Mathematical Methods for Pattern Recognition (MMPR-14), pp. 346–350 (2009). (in Russian)Google Scholar
  20. 20.
    Zhukova, K.V., Reyer, I.A.: Structure analysis of object shape with use of skeleton core. In: Proceedings of 8-th International Conference on Intelligent Information Processing (IIP-08), pp. 350–354 (2010). (in Russian)Google Scholar
  21. 21.
    Zhukova, K.V., Reyer, I.A.: Parametric family of skeleton bases of a polygonal figure. Mach. Learn. Data Anal. 1(4), 391–410 (2012). (in Russian)Google Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Federal Research Center “Computer Science and Control” of RASMoscowRussia

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