Sketch-Based Shape Retrieval Using Length and Curvature of 2D Digital Contours

  • Abdolah Chalechale
  • Golshah Naghdy
  • Prashan Premaratne
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3322)


This paper presents a novel effective method for line segment extraction using chain code differentiation. The resulting line segments are employed for shape feature extraction. Length distribution of the extracted segments along with distribution of the angle between adjacent segments are exploited to extract compact hybrid features. The extracted features are used for sketch-based shape retrieval. Comparative results obtained from six other well known methods within the literature have been discussed. Using MPEG-7 contour shape database (CE-1) as the test bed, the new proposed method shows significant improvement in retrieval performance for sketch-based shape retrieval. The Average Normalized Modified Retrieval Rank (ANMRR) is used as the performance indicator. Although the retrieval performance has been improved using the proposed method, its computational intensity and subsequently, its feature extraction time are slightly higher than some other methods.


Retrieval Performance Zernike Moment Fourier Descriptor Chain Code Corner Angle 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Pavlidis, T.: Survey: A review of algorithms for shape analysis. Computer Graphics and Image Processing 7, 243–258 (1978)CrossRefGoogle Scholar
  2. 2.
    Loncaric, S.: A survey of shape analysis techniques. Patt. Recog. 31, 1983–(1998)CrossRefGoogle Scholar
  3. 3.
    Jain, A.J., Vailaya, A.: Shape-based retrieval: a case study with trademark image databases. Patt. Recog. 31, 1369–1390 (1998)CrossRefGoogle Scholar
  4. 4.
    Bober, M.: MPEG-7 visual shape descriptors. IEEE Trans. Circ. and Syst. for Video Tech. 11, 716–719 (2001)CrossRefGoogle Scholar
  5. 5.
    Bimbo, A.D.: Visual Inform. retrieval. Morgan Kaufmann, San Francisco (1999)Google Scholar
  6. 6.
    Widrow, B.: The ”rubber-mask” technique-ii. pattern storage and recognition. Patt. Recog. 5, 199–211 (1973)CrossRefGoogle Scholar
  7. 7.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: active contour models. Int. Journal of Computer Vision 3, 321–331 (1988)CrossRefGoogle Scholar
  8. 8.
    Smith, J.R., Chang, S.F.: VisualSEEk: a fully automated content-based image query system. In: Proc. ACM Multimedia 1996, USA, pp. 87–98 (1996)Google Scholar
  9. 9.
    Zhang, D., Lu, G.: Evaluation of MPEG-7 shape descriptor against other shape descritors. Multimedia systems 9, 15–30 (2003)CrossRefGoogle Scholar
  10. 10.
    Kauppinen, H., Seppanen, T., Pietikainen, M.: An experimental comparison of autoregressive and Fourier-based descriptors in 2D shape classification. IEEE Trans. Patt. Anal. and Mach. Intel. 17, 201–207 (1995)CrossRefGoogle Scholar
  11. 11.
    ISO/IEC JTC1/SC29/WG11/N4358: Text of ISO/IEC 15938-3/FDIS information technology – multimedia content description interface – part 3 visual, Sydney (2001)Google Scholar
  12. 12.
    ISO/IEC JTC1/SC29/WG11/N3321: MPEG-7 visual part of experimentation model version 5, Nordwijkerhout (2000)Google Scholar
  13. 13.
    Hoynck, M., Ohm, J.R.: Shape retrieval with robustness against partial occlusion. In: IEEE Int. Conf. Acoustics, Speech, and Signal Processing, vol. 3, pp. 593–596 (2003)Google Scholar
  14. 14.
    Zhang, D., Lu, G.: Generic Fourier descriptor for shape-based image retrieval. In: Proc. IEEE Int. Conf. Multimedia and Expo., vol. 1, pp. 425–428 (2002)Google Scholar
  15. 15.
    Zhang, D., Lu, G.: Shape-based image retrieval using generic Fourier descriptor. Signal Processing: Image Commun. 17, 825–848 (2002)CrossRefMathSciNetGoogle Scholar
  16. 16.
    Matusiak, S., Daoudi, M., Blu, T., Avaro, O.: Sketch-based images database retrieval. In: Jajodia, S., Özsu, M.T., Dogac, A. (eds.) MIS 1998. LNCS, vol. 1508, p. 185. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  17. 17.
    Ip, H.H.S., Cheng, A.K.Y., Wong, W.Y.F., Feng, J.: Affine-invariant sketch-based retrieval of images. In: Proc. IEEE Int. Conf. Comput. Graphics, pp. 55–61 (2001)Google Scholar
  18. 18.
    Chalechale, A., Naghdy, G., Mertins, A.: Sketch-based image matching using angular partitioning. IEEE Trans. Systems, Man, Cybernetics - Part A: Systems and Humans (2004)Google Scholar
  19. 19.
    Chalechale, A., Naghdy, G., Premaratne, P.: Image database retrieval using sketched queries. In: Proc. IEEE Int. Conf. Image Processing (ICIP 2004), Singapore (2004)Google Scholar
  20. 20.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Addison-Wesley, Reading (1992)Google Scholar
  21. 21.
    Li, H., Manjunath, B.S., Mitra, S.K.: A contour-based approach to multisensor image registration. IEEE Trans. Image Processing 4, 320–334 (1995)CrossRefGoogle Scholar
  22. 22.
    ISO/IEC JTC1/SC29/WG11-MPEG2000/M5984: Core experiments on MPEG-7 edge histogram descriptors, Geneva (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Abdolah Chalechale
    • 1
  • Golshah Naghdy
    • 1
  • Prashan Premaratne
    • 1
  1. 1.School of Electrical, Computer & Telecommunications EngineeringUniversity of WollongongAustralia

Personalised recommendations