Skip to main content
Log in

The Representation and Matching of Images Using Top Points

  • Published:
Journal of Mathematical Imaging and Vision Aims and scope Submit manuscript

Abstract

In previous work, singular points (or top points) in the scale space representation of generic images have proven valuable for image matching. In this paper, we propose a novel construction that encodes the scale space description of top points in the form of a directed acyclic graph. This representation allows us to utilize coarse-to-fine graph matching algorithms for comparing images represented in terms of top point configurations instead of using solely the top points and their features in a point matching algorithm, as was done previously. The nodes of the graph represent the critical paths together with their top points. The edge set captures the neighborhood distribution of vertices in scale space, and is constructed through a hierarchical tessellation of scale space using a Delaunay triangulation of the top points. We present a coarse-to-fine many-to-many matching algorithm for comparing such graph-based representations. The algorithm is based on a metric-tree representation of labeled graphs and their low-distortion embeddings into normed vector spaces via spherical encoding. This is a two-step transformation that reduces the matching problem to that of computing a distribution-based distance measure between two such embeddings. To evaluate the quality of our representation, four sets of experiments are performed. First, the stability of this representation under Gaussian noise of increasing magnitude is examined. Second, a series of recognition experiments is run on a face database. Third, a set of clutter and occlusion experiments is performed to measure the robustness of the algorithm. Fourth, the algorithm is compared to a leading interest point-based framework in an object recognition experiment.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Balmashnova, E., Florack, L., ter Haar Romeny, B.: Feature vector similarity based on local structure. In: Sgallari, F., Murli, A., Paragios, N. (eds.) Scale Space and Variational Methods in Computer Vision: Proceedings of the First International Conference, SSVM 2007, Ischia, Italy. Lecture Notes in Computer Science, vol. 4485, pp. 386–393. Springer, Berlin (2007)

    Chapter  Google Scholar 

  2. Balmashnova, E., Florack, L.M.J., Platel, B., Kanters, F.M.W., ter Haar Romeny, B.M.: Stability of top-points in scale space. In: Kimmel, R., Sochen, N., Weickert, J. (eds.) Scale Space and PDE Methods in Computer Vision: Proceedings of the Fifth International Conference, Scale-Space 2005, Hofgeismar, Germany. Lecture Notes in Computer Science, vol. 3459, pp. 62–72. Springer, Berlin (2005)

    Google Scholar 

  3. Balmashnova, E., Platel, B., Florack, L., ter Haar Romeny, B.M.: Content-based image retrieval by means of scale-space top-points and differential invariants. In: Greenspan, H., Lehmann, T. (eds.) Proceedings of the MICCAI Workshop on Medical Content-Based Image Retrieval for Biomedical Image Archives: Achievements, Problems, and Prospects (Brisbane, Australia, October 29, 2007), pp. 83–92 (2007)

  4. Balmashnova, E., Platel, B., Florack, L.M.J., ter Haar Romeny, B.M.: Object matching in the presence of non-rigid deformations close to similarities. In: Proceedings of the 8th IEEE Computer Society Workshop on Non-Rigid Registration and Tracking through Learning, held in Conjunction with the IEEE International Conference on Computer Vision (Rio de Janeiro, Brazil, October 14–20, 2007. IEEE Computer Society Press, Los Alamitos (2007)

    Google Scholar 

  5. Bay, H., Ess, A., Tuytelaars, T., Gool, L.: Speeded-up robust features (surf). Comput. Vis. Image Underst. 110(3), 346–359 (2008)

    Article  Google Scholar 

  6. Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Trans. Pattern Anal. Mach. Intell. 24(24), 509–522 (2002)

    Article  Google Scholar 

  7. Bretzner, L., Lindeberg, T.: Qualitative multiscale feature hierarchies for object tracking. J. Vis. Commun. Image Represent. 11(2), 115–129 (2000)

    Article  Google Scholar 

  8. Carneiro, G., Jepson, A.D.: Phase-based local features. In: Proceedings of the 7th European Conference on Computer Vision, Part I, pp. 282–296. Springer, London (2002)

    Google Scholar 

  9. Cohen, S., Guibas, L.: The earth mover’s distance under transformation sets. In: ICCV’99: Proceedings of the International Conference on Computer Vision, vol. 2, p. 1076. IEEE Computer Society, Washington (1999)

    Chapter  Google Scholar 

  10. Crowley, J., Parker, A.: A representation for shape based on peaks and ridges in the difference of low-pass transform. IEEE Trans. Pattern Anal. Mach. Intell. 6(2), 156–170 (1984)

    Article  Google Scholar 

  11. Damon, J.: Local Morse theory for solutions to the heat equation and Gaussian blurring. J. Differ. Equ. 115(2), 368–401 (1995)

    Article  MATH  MathSciNet  Google Scholar 

  12. Demirci, M.F., Shokoufandeh, A., Dickinson, S., Keselman, Y., Bretzner, L.: Many-to-many matching of scale-space feature hierarchies using metric embedding. In: Proceedings, Scale Space Methods in Computer Vision, 4th International Conference, pp. 17–32, June 2003

  13. Demirci, M.F., Shokoufandeh, A., Dickinson, S., Keselman, Y., Bretzner, L.: Many-to-many feature matching using spherical coding of directed graphs. In: Proceedings, 8th European Conference on Computer Vision, pp. 332–335, May 2004

  14. Demirci, M.F., Shokoufandeh, A., Keselman, Y., Bretzner, L., Dickinson, S.: Object recognition as many-to-many feature matching. Int. J. Comput. Vis. 69(2), 203–222 (2006)

    Article  Google Scholar 

  15. Dufournaud, Y., Schmid, C., Horaud, R.: Matching images with different resolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 612–618. IEEE Computer Society Press, Los Alamitos (2000)

    Google Scholar 

  16. Eberly, D., Gardner, R., Morse, B., Pizer, S., Scharlach, C.: Ridges for image analysis. J. Math. Imaging Vis. 4(4), 353–373 (1994)

    Article  Google Scholar 

  17. Florack, L., Kuijper, A.: The topological structure of scale-space images. J. Math. Imaging Vis. 12(1), 65–79 (2000)

    Article  MATH  MathSciNet  Google Scholar 

  18. Geusebroek, J.M., Burghouts, G.J., Smeulders, A.W.M.: The Amsterdam library of object images. Int. J. Comput. Vis. 61(1), 103–112 (2005)

    Article  Google Scholar 

  19. Giannopoulos, P., Veltkamp, R.: A pseudo-metric for weighted point sets. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) Proceedings of the Seventh European Conference on Computer Vision (Copenhagen, Denmark, May–June 2002). Lecture Notes in Computer Science, vols. 2350–2353, pp. 715–730. Springer, Berlin (2002)

    Google Scholar 

  20. Hummel, R., Moniot, R.: Reconstructions from zero-crossings in scale-space. IEEE Trans. Acoust. Speech Signal Proces. 37(12), 2111–2130 (1989)

    Article  Google Scholar 

  21. Huttenlocher, D., Ullman, S.: Recognizing solid objects by alignment with an image. Int. J. Comput. Vis. 5(2), 195–212 (1990)

    Article  Google Scholar 

  22. Johansen, P.: On the classification of toppoints in scale space. J. Math. Imaging Vis. 4, 57–67 (1994)

    Article  Google Scholar 

  23. Johansen, P., Skelboe, S., Grue, K., Andersen, J.: Representing signals by their toppoints in scale space. In: Proceedings of the International Conference on Image Analysis and Pattern Recognition, pp. 215–217, 1986

  24. Kadir, T., Brady, M.: Saliency, scale and image description. Int. J. Comput. Vis. 45(2), 83–105 (2001)

    Article  MATH  Google Scholar 

  25. Kanters, F.M.W.: Scalespaceviz. http://www.bmi2.bmt.tue.nl/image-analysis/people/FKanters/Software/ScaleSpaceViz.html/ (2004)

  26. Kanters, F.M.W., Florack, L.M.J., Platel, B., ter Haar Romeny, B.M.: Image reconstruction from multiscale critical points. In: Proceedings of the 4th International Conference on Scale Space Methods in Computer Vision, pp. 464–478. Isle of Skye, UK (2003)

    Chapter  Google Scholar 

  27. Kanters, F.M.W., Platel, B., Florack, L.M.J., ter Haar Romeny, B.M.: Content based image retrieval using multiscale top points. In: Proceedings of the 4th International Conference on Scale Space Methods in Computer Vision, pp. 33–43. Isle of Skye, UK (2003)

    Chapter  Google Scholar 

  28. Keselman, Y., Shokoufandeh, A., Demirci, M.F., Dickinson, S.: Many-to-many graph matching via low-distortion embedding. In: Proceedings, Computer Vision and Pattern Recognition, pp. 850–857, 2003

  29. Koenderink, J.J.: The structure of images. Biol. Cybern. 50(5), 363–370 (1984)

    Article  MATH  MathSciNet  Google Scholar 

  30. Koenderink, J.J., van Doorn, A.J.: Dynamic shape. Biol. Cyber. 53(6), 383–396 (1986)

    Article  MATH  Google Scholar 

  31. Lamdan, Y., Schwartz, J., Wolfson, H.: Affine invariant model-based object recognition. IEEE Trans. Robot. Automat. 6(5), 578–589 (1990)

    Article  Google Scholar 

  32. Lifshitz, L.M., Pizer, S.M.: A multiresolution hierarchical approach to image segmentation based on intensity extrema. IEEE Trans. Pattern Anal. Mach. Intell. 12(6), 529–541 (1990)

    Article  Google Scholar 

  33. Lindeberg, T.: Scale-space behaviour of local extrema and blobs. J. Math. Imaging Vis. 1(1), 65–99 (1992)

    Article  MathSciNet  Google Scholar 

  34. Lindeberg, T.: Detecting salient blob-like image structures and their scales with a scale-space primal sketch: A method for focus-of-attention. Int. J. Comput. Vis. 11(3), 283–318 (1993)

    Article  Google Scholar 

  35. Lindeberg, T.: Scale-Space Theory in Computer Vision. Kluwer Academic, Norwell (1994)

    Google Scholar 

  36. Lindeberg, T.: Edge detection and ridge detection with automatic scale selection. Int. J. Comput. Vis. 30(2), 117–156 (1998)

    Article  Google Scholar 

  37. Lindeberg, T.: Feature detection with automatic scale selection. Int. J. Comput. Vis. 30(2), 77–116 (1998)

    Google Scholar 

  38. Loog, M., Duistermaat, J.J., Florack, L.: On the behavior of spatial critical points under Gaussian blurring. A folklore theorem and scale-space constraints. In: Scale-Space’01: Proceedings of the Third International Conference on Scale-Space and Morphology in Computer Vision, pp. 183–192. Springer, London (2001)

    Google Scholar 

  39. Lowe, D.: Perceptual Organization and Visual Recognition. Kluwer Academic, Norwell (1985)

    Google Scholar 

  40. Lowe, D.: Object recognition from local scale-invariant features. In: Proceedings of the International Conference on Computer Vision, vol. 2, pp. 1150–1157. IEEE Computer Society, Washington (1999)

    Chapter  Google Scholar 

  41. Matas, J., Chum, O., Martin, U., Pajdla, T.: Robust wide baseline stereo from maximally stable extremal regions. In: Proceedings of the British Machine Vision Conference, vol. 1, pp. 384–393, London (2002)

  42. Mikolajczyk, K., Schmid, C.: Scale & affine invariant interest point detectors. Int. J. Comput. Vis. 60(1), 63–86 (2004)

    Article  Google Scholar 

  43. Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., Van Gool, L.: A comparison of affine region detectors. Int. J. Comput. Vis. 65(1–2), 43–72 (2005)

    Article  Google Scholar 

  44. Nielsen, M., Lillholm, M.: What do features tell about images? In: Scale-Space ’01: Proceedings of the Third International Conference on Scale-Space and Morphology in Computer Vision, pp. 39–50. Springer, London (2001)

    Google Scholar 

  45. Okabe, A., Boots, B.: Spatial Tesselations: Concepts and Applications of Voronoi Diagrams. Wiley, New York (1992)

    MATH  Google Scholar 

  46. ORL. The ORL face database at the AT&T (Olivetti) research laboratory. http://www.cl.cam.ac.uk/Research/DTG/attarchive/facedatabase.html (1992)

  47. Platel, B., Balmashnova, E., Florack, L., ter Haar Romeny, B.M.: Top-points as interest points for image matching. In: Proceedings of the 9th European Conference on Computer Vision, pp. 418–429 (2006)

  48. Platel, B., Kanters, F.M.W., Florack, L.M.J., Balmashnova, E.G.: Using multiscale top points in image matching. In: Proceedings of the 11th International Conference on Image Processing, Singapore, October 2004

  49. Poston, T., Stewart, I.N.: Catastrophe Theory and its Applications. Pitman, London (1978)

    MATH  Google Scholar 

  50. Preparata, F., Shamos, M.: Computational Geometry. Springer, New York (1985)

    Google Scholar 

  51. Schmid, C., Mohr, R.: Local grayvalue invariants for image retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 19(5), 530–535 (1997)

    Article  Google Scholar 

  52. Shilane, P., Min, P., Kazhdan, M., Funkhouser, T.: The Princeton shape benchmark. In: Proceedings of the Shape Modeling International 2004, pp. 167–178. IEEE Computer Society, Washington (2004)

    Chapter  Google Scholar 

  53. Shokoufandeh, A., Bretzner, L., Macrini, D., Demirci, M.F., Jönsson, C., Dickinson, S.: The representation and matching of categorical shape. Comput. Vis. Image Understand. 103(2), 139–154 (2006)

    Article  Google Scholar 

  54. Shokoufandeh, A., Dickinson, S., Jönsson, C., Bretzner, L., Lindeberg, T.: On the representation and matching of qualitative shape at multiple scales. In: Proceedings of the 7th European Conference on Computer Vision, Part III, pp. 759–775. Springer, London (2002)

    Google Scholar 

  55. Shokoufandeh, A., Dickinson, S., Siddiqi, K., Zucker, S.: Indexing using a spectral encoding of topological structure. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 491–497. Fort Collins, Glasgow (1999)

    Google Scholar 

  56. Shokoufandeh, A., Marsic, I., Dickinson, S.: View-based object recognition using saliency maps. Image Vis. Comput. 17(5/6), 445–460 (1999)

    Article  Google Scholar 

  57. Siddiqi, K., Shokoufandeh, A., Dickinson, S., Zucker, S.: Shock graphs and shape matching. Int. J. Comput. Vis. 30, 1–24 (1999)

    Google Scholar 

  58. Thom, R.: Stabilité Structurelle et Morphogénèse. Benjamin, Paris (1972)

    Google Scholar 

  59. Thom, R.: Structural Stability and Morphogenesis. Benjamin–Addison Wesley, New York (1975) (translated by D.H. Fowler)

    MATH  Google Scholar 

  60. van Wijk, J.J., Nuij, W.A.A.: A model for smooth viewing and navigation of large 2d information spaces. IEEE Trans. Vis. Comput. Graph. 10(4), 447–458 (2004)

    Article  Google Scholar 

  61. Witkin, A.: Scale-space filtering. In: Proceedings of the International Joint Conference on Artificial Intelligence, pp. 1019–1022, 1983

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to M. Fatih Demirci.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Demirci, M.F., Platel, B., Shokoufandeh, A. et al. The Representation and Matching of Images Using Top Points. J Math Imaging Vis 35, 103–116 (2009). https://doi.org/10.1007/s10851-009-0157-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10851-009-0157-y

Keywords

Navigation