A New Minimum Trees-Based Approach for Shape Matching with Improved Time Computing: Application to Graphical Symbols Recognition

  • Patrick Franco
  • Jean-Marc Ogier
  • Pierre Loonis
  • Rémy Mullot
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6020)


Recently we have developed a model for shape description and matching. Based on minimum spanning trees construction and specifics stages like the mixture, it seems to have many desirable properties. Recognition invariance in front shift, rotated and noisy shape was checked through median scale tests related to GREC symbol reference database. Even if extracting the topology of a shape by mapping the shortest path connecting all the pixels seems to be powerful, the construction of graph induces an expensive algorithmic cost. In this article we discuss on the ways to reduce time computing. An alternative solution based on image compression concepts is provided and evaluated. The model no longer operates in the image space but in a compact space, namely the Discrete Cosine space. The use of block discrete cosine transform is discussed and justified. The experimental results led on the GREC2003 database show that the proposed method is characterized by a good discrimination power, a real robustness to noise with an acceptable time computing.


Document analysis Graphics Recognition Region Based Shape Descriptor Feature extraction Minimum Spanning Tree Discrete Cosine Transform Image Compression 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Fifth IAPR International Workshop on Graphics Recognition. Computer Vision Center, Barcelona, Catalonia, Spain, July 30-31 (2003)Google Scholar
  2. 2.
    Ahmed, N., Natarajan, T., Rao, K.: On image processing and a discrete cosine transform. IEEE Transactions on Computers C-23(1), 90–93 (1974)zbMATHCrossRefMathSciNetGoogle Scholar
  3. 3.
    Chen, W., Pratt, K.: Scene adaptative coder. IEEE Transactions on Communications COM-32, 225–232 (1984)CrossRefGoogle Scholar
  4. 4.
    Egmont-Petersen, M., de Ridder, D., Handels, H.: Image processing with neural networks-a review. Pattern Recognition 35, 2279–2301 (2002)zbMATHCrossRefGoogle Scholar
  5. 5.
    Franco, P., Ogier, J.-M., Loonis, P., Mullot, R.: A topological measure for image object recognition. In: Lladós, J., Kwon, Y.-B. (eds.) GREC 2003. LNCS, vol. 3088, pp. 279–290. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  6. 6.
    Franco, P., Ogier, J.-M., Loonis, P., Mullot, R.: Template matching by minimum spanning trees. In: 5th IAPR International Conference on Graphics Recognition (GREC 2003), Barcelone, Spain, pp. 341–352 (2003)Google Scholar
  7. 7.
    Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992) (6 edn., 1999)zbMATHGoogle Scholar
  8. 8.
    Guichard, J., Nasse, D.: Traitement des images numériques pour la réduction du débit binaire. Le Traitement du Signal - Actes des Forums de France Télécom Recherche (2), 1–15 (1994)Google Scholar
  9. 9.
    Guillas, S., Bertet, K., Ogier, J.M.: Towards an iterative classification based on concept lattice. In: Yahia, S.B., Nguifo, E.M., Belohlavek, R. (eds.) CLA 2006. LNCS (LNAI), vol. 4923, pp. 256–262. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  10. 10.
    Hero, A., Michel, O.: Robust estimation of point process intensity features using k-minimal spanning trees. In: IEEE International Symposium on Information Theory, Germany, June 1997, p. 74 (1997)Google Scholar
  11. 11.
    Hero, A., Michel, O.: Robust entropy estimation strategies based on edge weighted random graphs. In: SPIE, International Symposium on Optical Science, Engineering and Instrumentation, San Diego (July 1998)Google Scholar
  12. 12.
    Hero, A., Michel, O.: Asymptotic theory of greedy approximations to minimal k-point random graphs. IEEE Transactions on Information Theory IT 45, 1921–1939 (1921)CrossRefMathSciNetGoogle Scholar
  13. 13.
    Karger, D., Klein, P., Tarjan, R.: A randomized linear-time algorithm to find minimum spanning trees. Journal of the Association for Computing Machinery (ACM) 42(2), 321–328 (1995)zbMATHMathSciNetGoogle Scholar
  14. 14.
    Khotanzad, A., Hong, Y.H.: Rotation invariant image recognition using features selected via a systematic method. Pattern Recognition (23), 1089–1101 (1990)Google Scholar
  15. 15.
    Kresch, R., Malah, D.: Morphological reduction of skeleton redundancy. Signal Processing 38, 143–151 (1994)CrossRefGoogle Scholar
  16. 16.
    Lladós, J., Valveny, E., Sánchez, G., Martí, E.: Symbol recognition: Current advances and perspectives. In: Blostein, D., Kwon, Y.-B. (eds.) GREC 2001. LNCS, vol. 2390, pp. 104–128. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  17. 17.
    Loeffler, C., Ligtenberg, A., Moschytz, G.: Practical fast 1-d dct algorithms with 11 multiplications. In: International Conference on Acoustics, Speech, and Signal Processing (ICASSP 1989), pp. 988–991 (1989)Google Scholar
  18. 18.
    Mallat, S.G.: Theory for multiresolution signal decomposition: The wavelet representation. IEEE Transactions on PAMI 11(7), 674–693 (1989)zbMATHGoogle Scholar
  19. 19.
    Maragos, P., Shafer, R.: Morphological skeleton representations and coding of binary images. IEEE Transactions on Accoustics, Speach and Signal Processing 34(5), 1228–1244 (1986)CrossRefGoogle Scholar
  20. 20.
    Marchand-Maillet, S., Sharaiha, Y.M.: A minimum spanning tree approach to line image analysis. In: Proceedings of the 13th International Conference on Pattern Recognition, August 1996, vol. 2, pp. 225–230 (1996)Google Scholar
  21. 21.
    Oh, C., Ryu, Y.K.: Study on the center of rotation method based on minimum spanning tree matching for fingerprint recognition. Optical Engineering 43(4), 822–829 (2004)CrossRefGoogle Scholar
  22. 22.
    Osowski, S., Dinh Nghia, D.: Fourier and wavelet descriptors for shape recognition using neural networks-a comparative study. Pattern Recognition 35, 1949–1957 (2002)zbMATHCrossRefGoogle Scholar
  23. 23.
    Pei, S., Lin, C.: Normalisation of rotationally symmetric shapes for pattern recognition. Pattern Recognition (25), 913–920 (1992)Google Scholar
  24. 24.
    Pennebaker, W.B., Mitchell, J.L.: The JPEG Still Image Data Compression Standard. Van Nostrand Reinhold, New York (1993)Google Scholar
  25. 25.
    Rao, K., Yip, P.: Discrete Cosine Transforms - Algorithms, Advantages, Applications. Academic Press, Boston (1990)Google Scholar
  26. 26.
    Redmond, C., Yukich, J.E.: Limit theorems and rates of convergence for euclidean functionals. Annals of Applied Probability 4(4), 1057–1073 (1994)zbMATHCrossRefMathSciNetGoogle Scholar
  27. 27.
    Rény, A.: On measures of entropy and information. In: Symposium on Mathematics Statistics and Probabilities, Berkeley, pp. 547–561 (1961)Google Scholar
  28. 28.
    Serra, J.: Image analysis and mathematical morphology. Theoretical Advances, vol. 2. Academic Press, London (1988)Google Scholar
  29. 29.
    Soss, M.: On the size of the sphere on influence graph. PhD thesis, Mc Gill University Scholl of Computer Science Montreal (1998)Google Scholar
  30. 30.
    Tabbone, S., Wendling, L.: Recognition of symbols in grey level line drawings from an adaptation of the radon transform. In: The 17th International Conference on Pattern Recognition, Cambridge, UK, pp. 570–573 (2004)Google Scholar
  31. 31.
    Tombre, K., Lamiroy, B.: Graphics recognition - from re-engineering to retrieval. In: 7th International Conference on Document Analysis and Recognition (ICDAR 2003), pp. 148–156. IEEE Computer Society, Los Alamitos (2003)CrossRefGoogle Scholar
  32. 32.
    Tombre, K.: Graphics recognition: The last ten years and the next ten years. In: Liu, W., Lladós, J. (eds.) GREC 2005. LNCS, vol. 3926, pp. 422–426. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  33. 33.
    Toussaint, G.: The relative neighborhood graph of a finite planar set. Pattern Recognition, 261–268 (1980)Google Scholar
  34. 34.
    Vetterli, M., Kovacevic, J.: Wavelets and Subband Coding. Prentice Hall, Englewood Cliffs (1995)zbMATHGoogle Scholar
  35. 35.
    Wallace, G.: The jpeg still picture compression standard. Communications of the Association for Computing Machinery 34(4), 30–44 (1991)Google Scholar
  36. 36.
    Xu, Y., Olman, V., Xu, D.: Minimum spanning trees for gene expression data clustering. Genome Informatics (12), 24–33 (2001)Google Scholar
  37. 37.
    Ying, X., Uberbacher, E.C.: 2d image segmentation using minimum spanning trees. Image and Vision Computing 15(1), 47–57 (1997)CrossRefGoogle Scholar
  38. 38.
    Zahn, C.: Graph-theoretical method for detecting and describing gestalt clusters. IEEE Trans. on Computers, 68–86 (1971)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Patrick Franco
    • 1
  • Jean-Marc Ogier
    • 1
  • Pierre Loonis
    • 2
  • Rémy Mullot
    • 1
  1. 1.Laboratoire Informatique, Image, Interaction (L3I)UPRES EA 2118, Université de La RochelleLa Rochelle Cedex 1France
  2. 2.Laboratoire Electronique, Informatique et Image (LE2I)UMR CNRS 5158, Université de BourgogneNevers cedexFrance

Personalised recommendations