Shape-Based Invariant Feature Extraction for Object Recognition

  • Mingqiang YangEmail author
  • Kidiyo Kpalma
  • Joseph Ronsin
Part of the Intelligent Systems Reference Library book series (ISRL, volume 29)


The emergence of new technologies enables generating large quantity of digital information including images; this leads to an increasing number of generated digital images. Therefore it appears a necessity for automatic systems for image retrieval. These systems consist of techniques used for query specification and retrieval of images from an image collection. The most frequent and the most common means for image retrieval is the indexing using textual keywords. But for some special application domains and face to the huge quantity of images, keywords are no more sufficient or unpractical. Moreover, images are rich in content; so in order to overcome these mentioned difficulties, some approaches are proposed based on visual features derived directly from the content of the image: these are the content-based image retrieval (CBIR) approaches. They allow users to search the desired image by specifying image queries: a query can be an example, a sketch or visual features (e.g., colour, texture and shape). Once the features have been defined and extracted, the retrieval becomes a task of measuring similarity between image features. An important property of these features is to be invariant under various deformations that the observed image could undergo.

In this chapter, we will present a number of existing methods for CBIR applications. We will also describe some measures that are usually used for similarity measurement. At the end, and as an application example, we present a specific approach, that we are developing, to illustrate the topic by providing experimental results.


Object Recognition Medial Axis Shape Descriptor Zernike Moment Shape Representation 
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.
    Abbasi, S., Mokhtarian, F., Kittler, J.: Enhancing CSS-based shape retrieval for objects with shallow concavities. Image and Vision Computing 18(3), 199–211 (2000)CrossRefGoogle Scholar
  2. 2.
    Alajlan, N., Kamel, M.S., Freeman, G.: Multi-object image retrieval based on shape and topology. Signal Processing: Image Communication 21, 904–918 (2006)CrossRefGoogle Scholar
  3. 3.
    Alajlan, N., Rube, I.E., Kamel, M.S., Freeman, G.: Shape retrieval using triangle-area representation and dynamic space warping. Pattern Recognition 40(7), 1911–1920 (2007)CrossRefzbMATHGoogle Scholar
  4. 4.
    Arbter, K., Snyder, W., Burkhardt, H., Hirzinger, G.: Applications of affine-invariant Fourier descriptors to recognition of 3-D objects. IEEE Trans. Pattern Analysis and Machine Intelligence 12(7), 640–646 (1990)CrossRefGoogle Scholar
  5. 5.
    Arica, N., Vural, F.: BAS: a perceptual shape descriptor based on the beam angle statistics. Pattern Recognition Letters 24(9-10) (2003)Google Scholar
  6. 6.
    Badawy, O.E., Kamel, M.: Shape Retrieval using Concavity Trees. In: Proceedings of the 17th International Conference on Pattern Recognition, pp. 111–114 (2004)Google Scholar
  7. 7.
    Bauckhage, C., Tsotsos, J.K.: Bounding box splitting for robust shape classification. In: Proc. IEEE International Conference on Image Processing, pp. 478–481 (2005)Google Scholar
  8. 8.
    Belongie, S., Malik, J., Puzicha, J.: Shape Matching and Object Recognition Using Shape Context. IEEE Trans. Pattern Analysis and Machine Intelligence 24(4), 509–522 (2002)CrossRefGoogle Scholar
  9. 9.
    Berretti, S., Bimbo, A.D., Pala, P.: Retrieval by shape similarity with perceptual distance and effective indexing. IEEE Trans. on Multimedia 2(4), 225–239 (2000)CrossRefGoogle Scholar
  10. 10.
    Celebi, M.E., Aslandogan, Y.A.: A Comparative Study of Three Moment-Based Shape Descriptors. In: Proc. of the International Conference of Information Technology: Codingand Computing, pp. 788–793 (2005)Google Scholar
  11. 11.
    Chakrabarti, K., Binderberger, M., Porkaew, K., Mehrotra, S.: Similar shape retrieval in MARS. In: Proc. IEEE International Conference on Multimedia and Expo. (2000)Google Scholar
  12. 12.
    Chen, G., Bui, T.D.: Invariant Fourier-wavelet descriptor for pattern recognition. Pattern Recognition 32, 1083–1088 (1999)CrossRefGoogle Scholar
  13. 13.
    Chuang, C.-H., Kuo, C.-C.: Wavelet Descriptor of Planar Curves: Theory and Applications. IEEE Trans. Image Processing 5(1), 56–70 (1996)CrossRefGoogle Scholar
  14. 14.
    Davies, E.: Machine Vision: Theory, Algorithms, Practicalities. Academic Press, New York (1997)Google Scholar
  15. 15.
    Dubinskiy, A., Zhu, S.C.: A Multi-scale Generative Model for Animate Shapes and Parts. In: Proc. Ninth IEEE International Conference on Computer Vision, ICCV (2003)Google Scholar
  16. 16.
    Gonzalez, R., Woods, R.: Digital image processing, 2nd edn. Pearson Education North Asia Limited and Publishing House of Electronics Industry (2002)Google Scholar
  17. 17.
    Guru, D., Nagendraswam, H.: Symbolic representation of two-dimensional shapes. Pattern Recognition Letters 28, 144–155 (2007)CrossRefGoogle Scholar
  18. 18.
    Han, S., Yang, S.: An Invariant Feature Representation for shape Retrieval. In: Proc. Sixth International Conference on Parallel and Distributed Computing, Applications and Technologies (2005)Google Scholar
  19. 19.
    Jalba, A., Wilkinson, M., Roerdink, J.: Shape representation and recognition through morphological curvature scale spaces. IEEE Trans. Image Processing 15(2), 331–341 (2006)CrossRefGoogle Scholar
  20. 20.
    Jin, K., Cao, M., Kong, S., Lu, Y.: Homocentric Polar-Radius Moment for Shape Classification. In: The 8th International Conference on Proc. Signal Processing (2006)Google Scholar
  21. 21.
    Kan, C., Srinath, M.D.: Invariant character recognition with Zernike and orthogonal Fourier-Mellin moments. Pattern Recognition 35, 143–154 (2002)CrossRefzbMATHGoogle Scholar
  22. 22.
    Kauppinen, H., Seppanen, T., Pietikainen, M.: An Experimental Comparison of Auto-regressive and Fourier-Based Descriptors in 2-D Shape Classification. IEEE Trans. Pattern Analysis and Machine Intelligence 17(2), 201–207 (1995)CrossRefGoogle Scholar
  23. 23.
    Khalil, M., Bayoumi, M.: A Dyadic Wavelet Affine Invariant Function for 2D Shape Recognition. IEEE Trans. Pattern Analysis and Machine Intelligence 25(10), 1152–1164 (2001)CrossRefGoogle Scholar
  24. 24.
    Kpalma, K., Ronsin, J.: Multiscale contour description for pattern recognition. Pattern Recognition Letters 27, 1545–1559 (2006)CrossRefGoogle Scholar
  25. 25.
    Latecki, L.J., Lakamper, R.: Shape Similarity Measure Based on Correspondence of Visual Parts. IEEE Trans. Pattern Analysis and Machine Intelligence 22(10), 1185–1190 (2000)CrossRefGoogle Scholar
  26. 26.
    Latecki, L.J., Lakamper, R.: Convexity rule for shape decomposition based on discrete Contour Evolution. Computer Vision and Image Understanding 73(3), 441–454 (1999)CrossRefGoogle Scholar
  27. 27.
    Lee, S.-M., Abbott, A.L., Clark, N.A., Araman, P.A.: A Shape Representation for Planar Curves by Shape Signature Harmonic Embedding. In: Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2006)Google Scholar
  28. 28.
    Liu, Y.K., Wei, W., Wang, P.J., Zalik, B.: Compressed vertex chain codes. Pattern Recognition 40(11), 2908–2913 (2007)CrossRefzbMATHGoogle Scholar
  29. 29.
    Lu, G., Sajjanhar, A.: Region-based shape representation and similarity measure suitable for content based image retrieval. ACM Multimedia System Journal 7(2), 165–174 (1999)CrossRefGoogle Scholar
  30. 30.
    Lu, K.-J., Kota, S.: Compliant Mechanism Synthesis for Shape-Change Applications: Preliminary Results. In: Proceedings of SPIE Modelling, Signal Processing, and Control Conference, pp. 161–172 (2002)Google Scholar
  31. 31.
    Mehtre, B.M., Kankanhalli, M.S., Lee, W.F.: Shape Measures for Content Based Image Retrieval: A Comparison. Pattern Recognition 33(3), 319–337 (1997)Google Scholar
  32. 32.
    Mokhtarian, F., Mackworth, A.K.: A Theory of Multiscale, Curvature-Based Shape Representation for Planar Curves. IEEE Trans. Pattern Analysis and Machine Intelligence 14(8), 789–805 (1992)CrossRefGoogle Scholar
  33. 33.
    Mori, G., Malik, J.: Estimating Human Body Configurations Using Shape Context Matching. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2352, pp. 666–680. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  34. 34.
    Mukundan, R.: A new class of rotational invariants using discrete orthogonal moments. In: Sixth IASTED International Conference on Signal and Image Processing, pp. 80–84 (2004)Google Scholar
  35. 35.
    Mukundan, R., Ong, S., Lee, P.: Image Analysis by Tchebichef Moments. IEEE Trans. Image Processing 10(9), 1357–1364 (2001)CrossRefzbMATHMathSciNetGoogle Scholar
  36. 36.
    Peng, J., Yang, W., Cao, Z.: A Symbolic Representation for Shape Retrieval in Curvature Scale Space. In: Proc. International Conference on Computational Intelligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce (2006)Google Scholar
  37. 37.
    Ricard, J., Coeurjolly, D., Baskurt, A.: Generalizations of angular radial transform for 2D and 3D shape retrieval. Pattern Recognition Letters 26(14) (2005)Google Scholar
  38. 38.
    Sebastian, T., Klein, P., Kimia, B.: Recognition of Shapes by Editing Their Shock Graphs. IEEE Trans. Pattern Analysis and Machine Intelligence 26(5), 550–571 (2004)CrossRefGoogle Scholar
  39. 39.
    Siddiqi, K., Kimia, B.: A Shock Grammar for Recognition. In: Proceedings of the IEEE Conference Computer Vision and Pattern Recognition, pp. 507–513 (1996)Google Scholar
  40. 40.
    Smith, S.P., Jain, A.K.: Chord distribution for shape matching. Computer Graphics and Image Processing 20, 259–271 (1982)CrossRefGoogle Scholar
  41. 41.
    Sonka, M., Hlavac, V., Boyle, R.: Image Processing, Analysis and Machine Vision. Chapman and Hall, London (1993)Google Scholar
  42. 42.
    Tabbone, S., Wendling, L., Salmon, J.-P.: A new shape descriptor defined on the Radon transform. Computer Vision and Image Understanding 102(1), 42–51 (2006)CrossRefGoogle Scholar
  43. 43.
    Taubin, G., Cooper, D.: Recognition and positioning of rigid objects using algebraic moment invariants. In: SPIE Conference on Geometric Methods in Computer Vision, pp. 175–186 (1991)Google Scholar
  44. 44.
    Taza, A., Suen, C.: Discrimination of planar shapes using shape matrices. IEEE Trans. System, Man, and Cybernetics 19(5), 1281–1289 (1989)CrossRefGoogle Scholar
  45. 45.
    Thayananthan, A., Stenger, B., Torr, P.H.S., Cipolla, R.: Shape Context and Chamfer Matching in Cluttered Scenes. In: Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2003)Google Scholar
  46. 46.
    Tieng, Q.M., Boles, W.W.: Wavelet-Based Affine Invariant Representation: A Tool for Recognizing PlanarObjects in 3D Space. IEEE Trans. Pattern Analysis and Machine Intelligence 19(8), 846–857 (1997)CrossRefGoogle Scholar
  47. 47.
    Wang, Y.P., Lee, K.T.: Multiscale curvature-based shape representation using B-spline wavelets. IEEE Trans. Image Process 8(10), 1586–1592 (1999)CrossRefGoogle Scholar
  48. 48.
    Yadava, R.B., Nishchala, N.K., Gupta, A.K.: Retrieval and classification of shape-based objects using Fourier, generic Fourier, and wavelet-Fourier descriptors technique: A comparative study. Optics and Lasers in Engineering 45(6), 695–708 (2007)CrossRefGoogle Scholar
  49. 49.
    Yang, M., Kpalma, K., Ronsin, J.: Scale-controlled area difference shape descriptor. In: Proc. SPIE, Electronic Imaging science and Technology (2007)Google Scholar
  50. 50.
    Zahn, C.T., Roskies, R.Z.: Fourier Descriptors for Plane closed Curves. IEEE Trans. Computer c-21(3), 269–281 (1972)CrossRefMathSciNetGoogle Scholar
  51. 51.
    Zhang, D., Lu, G.: Review of shape representation and description techniques. Pattern Recognition 37, 1–19 (2004)CrossRefzbMATHGoogle Scholar
  52. 52.
    Zhang, D., Lu, G.: A comparative study of curvature scale space and Fourier descriptors for shape-based image retrieval. Visual Communication and Image Representation 14(1) (2003)Google Scholar
  53. 53.
    Zhang, D., Lu, G.: A Comparative Study of Fourier Descriptors for Shape Representation and Retrieval. In: Proc. 5th Asian Conference on Computer Vision (2002)Google Scholar
  54. 54.
    Zhang, D.S., Lu, G.: A Comparative Study on Shape Retrieval Using Fourier Descriptors with DifferentShape Signatures. In: Proc. International Conference on Intelligent Multimedia and Distance Education (ICIMADE 2001) (2001)Google Scholar
  55. 55.
    Zhang, H., Malik, J.: Learning a discriminative classifier using shape context distances. In: Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2003)Google Scholar
  56. 56.
    ISO/IEC JTC1/SC29/WG11, MPEG-7 Overview (version 10), Technical report (2004)Google Scholar
  57. 57.
    Hu, M.-K.: Visual Pattern Recognition by Moment Invariants. IRE Trans. Information Theory IT-8, 179–187 (1962)Google Scholar
  58. 58.
    Iivarinen, J., Visa, A.: Shape recognition of irregular objects. In: Proc. SPIE, Intelligent Robots and Computer Vision XV: Algorithms, Techniques, Active Vision, and Materials Handling, pp. 25–32 (1996)Google Scholar
  59. 59.
    Flusser, J.: Invariant Shape Description and Measure of Object Similarity. In: Proc. 4th International Conference on Image Processing and its Applications, pp. 139–142 (1992)Google Scholar
  60. 60.
    di Baja, G.S., Thiel, E.: Skeletonization algorithm running on path-based distance maps. Image Vision Computer 14, 47–57 (1996)CrossRefGoogle Scholar
  61. 61.
    Borgefors, G.: Distance Transformations in Digital Images. Computer Vision, Graphics, and Image Processing, 344–371 (1986)Google Scholar
  62. 62.
    Kolesnikov, A.: Efficient algorithms for vectorization and polygonal approximation, Ph.D thesis, University of Joensu, Finland (2003)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.ISE, Shandong UniversityJinanChina
  2. 2.INSA, IETR, UMR 6164Université Européenne de BretagneRennesFrance

Personalised recommendations