Multiscale Shape Description with Laplacian Profile and Fourier Transform

  • Evanthia MavridouEmail author
  • James L. Crowley
  • Augustin Lux
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8814)


We propose a new local multiscale image descriptor of variable size. The descriptor combines Laplacian of Gaussian values at different scales with a Radial Fourier Transform. This descriptor provides a compact description of the appearance of a local neighborhood in a manner that is robust to changes in scale and orientation. We evaluate this descriptor by measuring repeatability and recall against 1-precision with the Affine Covariant Features benchmark dataset and as well as with a set of textureless images from the MIRFLICKR Retrieval Evaluation dataset. Experiments reveal performance competitive to the state of the art, while providing a more compact representation.


Robust image description Scale invariance Local appearance description Compact descriptor Variable vector length 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Alahi, A., Ortiz, R., Vandergheynst, P.: FREAK: Fast Retina Keypoint. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 510–517 (June 2012)Google Scholar
  2. 2.
    Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-Up Robust Features (SURF). Comput. Vis. Image Underst. 110(3), 346–359 (2008). CrossRefGoogle Scholar
  3. 3.
    Bradski, G.: The OpenCV Library. Dr. Dobb’s Journal of Software Tools (2000)Google Scholar
  4. 4.
    Byrne, J., Shi, J.: Nested Shape Descriptors. In: 2013 IEEE International Conference on Computer Vision (ICCV), pp. 1201–1208 (December 2013)Google Scholar
  5. 5.
    Calonder, M., Lepetit, V., Ozuysal, M., Trzcinski, T., Strecha, C., Fua, P.: BRIEF: Computing a Local Binary Descriptor Very Fast. IEEE Trans. on Pattern Analysis and Machine Intelligence 34(7), 1281–1298 (2012)CrossRefGoogle Scholar
  6. 6.
    Crowley, J.L., Stern, R.M.: Fast Computation of the Difference of Low-Pass Transform. IEEE Trans. on Pattern Analysis and Machine Intelligence PAMI 6(2), 212–222 (1984)CrossRefzbMATHGoogle Scholar
  7. 7.
    Hall, D., Colin de Verdière, V., Crowley, J.L.: Object recognition using coloured receptive fields. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1842, pp. 164–177. Springer, Heidelberg (2000). CrossRefGoogle Scholar
  8. 8.
    Huiskes, M.J., Lew, M.S.: The MIR Flickr Retrieval Evaluation. In: MIR. ACM, New York (2008)Google Scholar
  9. 9.
    Juan, L., Gwon, O.: A Comparison of SIFT, PCA-SIFT and SURF. International Journal of Image Processing (IJIP) 3(4), 143–152 (2009)Google Scholar
  10. 10.
    Leutenegger, S., Chli, M., Siegwart, R.: BRISK: Binary Robust invariant scalable keypoints. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 2548–2555 (November 2011)Google Scholar
  11. 11.
    Lindeberg, T.: On the axiomatic foundations of linear scale-space: Combining semi-group structure with causality vs. scale invariance, Technical report, Department of Numerical Analysis and Computing Science, Royal Institute of Technology, S-100 44 Stockholm, Sweden, August 1994. (ISRN KTH NA/P-94/20-SE). Revised version published as Chapter 6 in Sporring, J., Nielsen, M., Florack, L., Johansen, P. (eds.): Gaussian Scale-Space Theory: Proc. PhD School on Scale-Space Theory, (Copenhagen, Denmark, May 1996), pp. 75–98. Kluwer Academic Publishers (1997)Google Scholar
  12. 12.
    Lowe, D.: Object recognition from local scale-invariant features. In: The Seventh IEEE International Conference on Computer Vision, vol. 2, pp. 1150–1157 (1999)Google Scholar
  13. 13.
    Mavridou, E., Hoàng, M.D., Crowley, J.L., Lux, A.: Scale normalized radial fourier transform as a robust image descriptor. In: ICPR (in press, 2014)Google Scholar
  14. 14.
    Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Trans. on Pattern Analysis and Machine Intelligence 27(10), 1615–1630 (2005)CrossRefGoogle Scholar
  15. 15.
    Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., Van Gool, L.: A Comparison of Affine Region Detectors. International Journal of Computer Vision 65(1–2), 43–72 (2005). CrossRefGoogle Scholar
  16. 16.
    Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: An Efficient Alternative to SIFT or SURF. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 2564–2571 (November 2011)Google Scholar
  17. 17.
    Ruiz-Hernandez, J.A., Lux, A., Crowley, J.L.: Face detection by cascade of Gaussian derivates classifiers calculated with a Half-Octave Pyramid. In: 8th IEEE International Conference on Automatic Face Gesture Recognition, FG 2008, pp. 1–6 (September 2008)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Evanthia Mavridou
    • 1
    • 2
    Email author
  • James L. Crowley
    • 1
    • 2
  • Augustin Lux
    • 1
    • 2
  1. 1.University of Grenoble Alpes, LIGGrenobleFrance
  2. 2.Inria Grenoble Rhône-Alpes Research Centre, LIGGrenobleFrance

Personalised recommendations