Advertisement

Progressive Mode-Seeking on Graphs for Sparse Feature Matching

  • Chao Wang
  • Lei Wang
  • Lingqiao Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8690)

Abstract

Sparse feature matching poses three challenges to graph-based methods: (1) the combinatorial nature makes the number of possible matches huge; (2) most possible matches might be outliers; (3) high computational complexity is often incurred. In this paper, to resolve these issues, we propose a simple, yet surprisingly effective approach to explore the huge matching space in order to significantly boost true matches while avoiding outliers. The key idea is to perform mode-seeking on graphs progressively based on our proposed guided graph density. We further design a density-aware sampling technique to considerably accelerate mode-seeking. Experimental study on various benchmark data sets demonstrates that our method is several orders faster than the state-of-the-art methods while achieving much higher precision and recall.

Keywords

Feature matching Mode-seeking 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Cho, M., Lee, J., Lee, K.M.: Feature correspondence and deformable object matching via agglomerative correspondence clustering. In: ICCV (2009)Google Scholar
  2. 2.
    Cho, M., Lee, J., Lee, K.M.: Reweighted random walks for graph matching. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part V. LNCS, vol. 6315, pp. 492–505. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  3. 3.
    Cho, M., Lee, K.M.: Authority-shift clustering: Hierarchical clustering by authority seeking on graphs. In: CVPR (2010)Google Scholar
  4. 4.
    Cho, M., Lee, K.M.: Mode-seeking on graphs via random walks. In: CVPR (2012)Google Scholar
  5. 5.
    Cho, M., Lee, K.M.: Progressive graph matching: Making a move of graphs via probabilistic voting. In: CVPR (2012)Google Scholar
  6. 6.
    Cho, M., Shin, Y.M., Lee, K.M.: Co-recognition of image pairs by data-driven monte carlo image exploration. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part IV. LNCS, vol. 5305, pp. 144–157. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  7. 7.
    Comaniciu, D., Meer, P.: A robust approach toward feature space analysis. TPAMI 24(5), 603–619 (2002)CrossRefGoogle Scholar
  8. 8.
    Conte, D., Foggia, P., Sansone, C., Vento, M.: Thirty years of graph matching in pattern recognition. IJPRAI, 265–298 (2004)Google Scholar
  9. 9.
    Duchenne, O., Bach, F., Kweon, I., Ponce, J.: A tensor-based algorithm for high-order graph matching. In: CVPR (2009)Google Scholar
  10. 10.
    Duchenne, O., Joulin, A., Ponce., J.: A graph-matching kernel for object categorization. In: ICCV (2011)Google Scholar
  11. 11.
    Ferrari, V., Tuytelaars, T., Gool, L.V.: Simultaneous object recognition and segmentation from single or multiple model views. IJCV 67(2), 159–188 (2006)CrossRefGoogle Scholar
  12. 12.
    Freedman, D., Kisilev, P.: Fast mean shift by compact density representation. In: CVPR (2009)Google Scholar
  13. 13.
    Georgescu, B., Shimshoni, I., Meer, R.: Mean shift based clustering in high dimensions: A texture classification example. In: ICCV (2003)Google Scholar
  14. 14.
    Jouili, S., Tabbone, S., Lacroix, V.: Median graph shift: A new clustering algorithm for graph domain. In: ICPR (2010)Google Scholar
  15. 15.
    Lee, J., Cho, M., Lee, K.M.: Hyper-graph matching via reweighted random walks. In: CVPR (2011)Google Scholar
  16. 16.
    Leordeanu, M., Hebert, M.: A spectral technique for correspondence problems using pairwise constraints. In: ICCV (2005)Google Scholar
  17. 17.
    Liu, H., Latecki, L.J., Yan, S.: Fast detection of dense subgraph with iterative shrinking and expansion. TPAMI (2013)Google Scholar
  18. 18.
    Lowe, D.G.: Object recognition from local scale-invariant features. In: ICCV (1999)Google Scholar
  19. 19.
    Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide baseline stereo from maximally stable extremal regions. In: BMVC (2002)Google Scholar
  20. 20.
    Mikolajczyk, K., Schmid, C.: Scale and affine invariant interest point detectors. In: IJCV (2004)Google Scholar
  21. 21.
    Paris, S., Durand, F.: A topological approach to hierarchical segmentation using mean shift. In: CVPR (2007)Google Scholar
  22. 22.
    Torresani, L., Kolmogorov, V., Rother, C.: Feature correspondence via graph matching: Models and global optimization. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 596–609. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  23. 23.
    Zass, R., Shashua, A.: Probabilistic graph and hypergraph matching. In: CVPR (2008)Google Scholar
  24. 24.
    Zhang, W., Wang, X., Zhao, D., Tang, X.: Graph degree linkage: agglomerative clustering on a directed graph. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part I. LNCS, vol. 7572, pp. 428–441. Springer, Heidelberg (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Chao Wang
    • 1
  • Lei Wang
    • 1
  • Lingqiao Liu
    • 2
  1. 1.School of Computer Science & Software EngineeringUniversity of WollongongAustralia
  2. 2.School of Computer ScienceUniversity of AdelaideAustralia

Personalised recommendations