A Study of Zernike Invariants for Content-Based Image Retrieval

  • Pablo Toharia
  • Oscar D. Robles
  • Ángel Rodríguez
  • Luis Pastor
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4872)


This paper presents a study about the application of Zernike invariants to content-based Image Retrieval for 2D color images. Zernike invariants have been chosen because of their good performance for object recognition. Taking into account the good results achieved in previous CBIR experiments with color based primitives using a multiresolution representation of the visual contents, this paper presents the application of a wavelet transform to the images in order to obtain a multiresolution representation of the shape based features studied. Experiments have been performed using two databases: the first one is a small self-made 2D color database formed by 298 RGB images and a test set with 1655 query images that has been used for preliminary tests; the second one is Also experiments using the Amsterdam Library of Object Images (ALOI), a free access database. Experimental results show the feasibility of this new approach.


CBIR primitives Zernike invariants 


  1. 1.
    del Bimbo, A.: Visual Information Retrieval. Morgan Kaufmann Publishers, San Francisco, California (1999)Google Scholar
  2. 2.
    Venters, C.C., Cooper, M.: A review of content-based image retrieval systems. Technical report, Manchester Visualization Center. Manchester Computing. University of Manchester (2000),
  3. 3.
    Marques, O., Furht, B.: Content-based Image and Video Retrieval. Multimedia Systems and Application Series. Kluwer Academic Publishers, Dordrecht (2002)zbMATHGoogle Scholar
  4. 4.
    Wu, J.-K., Kankanhalli, M.S., Wu, K.W.J.K., Lim, L.J.-H., Hong, H.D.: Perspectives on Content-Based Multimedia Systems. Springer, Heidelberg (2000)Google Scholar
  5. 5.
    Novotni, M., Klein, R.: 3D zernike descriptors for content based shape retrieval. In: The 8th ACM Symposium on Solid Modeling and Applications (2003)Google Scholar
  6. 6.
    Novotni, M., Klein, R.: Shape retrieval using 3D zernike descriptors. Computer-Aided Design 36(11), 1047–1062 (2004)CrossRefGoogle Scholar
  7. 7.
    Kim, H.K., Kim, J.D., Sim, D.G., Oh, D.I.: A modified zernike moment shape descriptor invariant to translation, rotation and scale for similarity-based image retrieval. In: International Conference on Multimedia and Expo, ICME, vol. 1, pp. 307–310 (2000)Google Scholar
  8. 8.
    Lin, T.W., Chou, Y.F.: A comparative study of zernike moments. In: Proceedings of the IEEE/WIC International Conference on Web Intelligence (WI’ 2003), Halifax, Canada (2003)Google Scholar
  9. 9.
    Hwang, S.K., Kim, W.Y.: A novel approach to the fast computation of Zernike moments. Pattern Recognition 39(11), 2065–2076 (2006)zbMATHCrossRefGoogle Scholar
  10. 10.
    Papakostas, G., Boutalis, Y., Karras, D., Mertzios, B.: A new class of Zernike moments for computer vision applications. Information Sciences 177(13), 2802–2819 (2007)zbMATHCrossRefMathSciNetGoogle Scholar
  11. 11.
    Wee, C.-Y., Paramesran, R.: On the computational aspects of Zernike moments. Image and Vision Computing 25(6), 967–980 (2007)CrossRefGoogle Scholar
  12. 12.
    Xin, Y., Pawlak, M., Liao, S.: Accurate computation of zernike moments in polar coordinates. IEEE Transactions on Image Processing 16(2), 581–587 (2007)CrossRefGoogle Scholar
  13. 13.
    Robles, O.D., Rodríguez, A., Córdoba, M.L.: A study about multiresolution primitives for content-based image retrieval using wavelets. In Hamza, M.H., ed.: IASTED International Conference On Visualization, Imaging, and Image Processing (VIIP 2001), Marbella, Spain, IASTED, ACTA Press, pp. 506–511 (2001) ISBN 0-88986-309-1Google Scholar
  14. 14.
    Strang, G., Nguyen, T.: Wavelets and Filter Banks. Wellesley-Cambridge Press (1997)Google Scholar
  15. 15.
    Starck, J.L., Murtagh, F., Bijaoul, A.: Image Processing and Data Analysis. The Multiscale Approach. Cambridge University Press, Cambridge (1998)Google Scholar
  16. 16.
    Rosenfeld, A.: Multiresolution Image Processing and Analysis. Springer Series in Information Sciences, vol. 12. Springer, Heidelberg (1984)zbMATHGoogle Scholar
  17. 17.
    Marr, D., Hildreth, E.: Theory of edge detection. In: Proceedings of the Royal Society, London, ser. B, vol. 207, pp. 187–217 (1980)Google Scholar
  18. 18.
    Daubechies, I.: Ten Lectures on Wavelets. vol. 61 of CBMS-NSF Regional Conf. Series in Appl. Math. Society for Industrial and Applied Mathematics, Philadelphia, PA (1992)Google Scholar
  19. 19.
    Mallat, S.G.: A theory for multiresolution signal decomposition: The wavelet representation. IEEE Trans. on Pattern Analysis and Machine Intelligence 11(7), 674–693 (1989)zbMATHCrossRefGoogle Scholar
  20. 20.
    Pastor, L., Rodríguez, A., Ríos, D.: Wavelets for Object Representation and Recognition in Computer Vision. In: Vidaković, B., Müller, P. (eds.) Bayesian Inference in Wavelet Based Models. Lectures Notes in Statistics, vol. 141, pp. 267–290. Springer Verlag, New York (1999)Google Scholar
  21. 21.
    Khotanzad, A., Hong, Y.H.: Invariant image recognition by zernike moments. IEEE Transactions on Pattern Analysis and Machine Intelligence 12(5), 489–497 (1990)CrossRefGoogle Scholar
  22. 22.
    Kamila, N.K., Mahapatra, S., Nanda, S.: Invariance image analysis using modified Zernike moments. Pattern Recognition Letters 26(6), 747–753 (2005)CrossRefGoogle Scholar
  23. 23.
    Zernike, F.: Beugungstheorie des schneidenverfahrens und seiner verbesserten form, der phasenkontrastmethode (Diffraction theory of the cut procedure and its improved form, the phase contrast method). Physica 1, 689–704 (1934)zbMATHCrossRefGoogle Scholar
  24. 24.
    Rodríguez, A., Robles, O.D., Pastor, L.: New features for Content-Based Image Retrieval using wavelets. In: Muge, F., Pinto, R.C., Piedade, M. (eds.) V Ibero-american Simposium on Pattern Recognition, SIARP 2000, Lisbon, Portugal, pp. 517–528 (2000) ISBN 972-97711-1-1Google Scholar
  25. 25.
    MIT Media Lab.: VisTex. Web Color image database (1998),
  26. 26.
    Over, P., Ianeva, T., Kraaij, W., Smeaton, A.F.: TRECVID 2006: Search task overview. In: Proceedings of the TRECVID Workshop, NIST Special Publication (2006),
  27. 27.
    Geusebroek, J.-M., Burghouts, G.J., Smeulders, A.W.M.: The Amsterdam library of object images. Int. J. Comput. Vision 61(1), 103–112 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Pablo Toharia
    • 1
  • Oscar D. Robles
    • 1
  • Ángel Rodríguez
    • 2
  • Luis Pastor
    • 1
  1. 1.Dpto. de Arquitectura y Tecnología de Computadores, Ciencias de la Computación e Inteligencia Artificial, U. Rey Juan Carlos, C/ Tulipán, s/n. 28933 Móstoles. Madrid.Spain
  2. 2.Dpto. de Tecnología Fotónica, U. Politécnica de Madrid, Campus de Montegancedo s/n, 28660 Boadilla del Monte, MadridSpain

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