Skip to main content
Log in

FPTAS for Minimizing the Earth Mover’s Distance Under Rigid Transformations and Related Problems

  • Published:
Algorithmica Aims and scope Submit manuscript

Abstract

In this paper, we consider the problem (denoted as EMDRT) of minimizing the earth mover’s distance between two sets of weighted points A and B in \({\mathbb {R}}^{d}\) under rigid transformation. EMDRT is an important problem in both theory and applications and has received considerable attentions in recent years. Previous research on this problem has resulted in only constant factor approximations and it has been an open problem for a long time to achieve PTAS solution. In this paper, we present the first FPTAS algorithm for EMDRT. Our algorithm runs roughly in \(O((nm)^{d+2}(\log nm)^{2d})\) time (which is close to a lower bound on any PTAS for this problem), where n and m are the sizes of A and B, respectively. Our result is based on several new techniques, such as the Sequential Orthogonal Decomposition and Optimum Guided Base, and can be extended to several related problems, such as the problem of earth mover’s distance under similarity transformation and the alignment problem, to achieve FPTAS for each of them.

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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Notes

  1. Note that [17, 23] also analyze the rigid transformations in a step-by-step fashion, but their methods are only for unweighted point-sets.

  2. We just need to determine which point belongs to the base from B, rather than specify the position of each point.

  3. This lemma might have been studied in some other papers or presented in other forms; for self-completeness, we present our proof here.

  4. In \({\mathbb {R}}^d\), a j-dimensional flat for any \(j<d\) is a j-dimensional subspace translated by some vector.

  5. Actually, another reason causing stop is that \(q_{b(l)}\) locates on the flat \(span\{q_{b(1)}, \ldots , \) \(q_{b(l-1)}\}\) for some \(2\le l\le d\). If that happens, recalling the Algorithm Base-Selection, we know that the whole B would locate on the \((l-2)\)-dimensional flat. In this case we can assume that \({ SOD}(B, R, f)\) does not stop in the following \(d-l+1\) steps, but the corresponding \(d-l+1\) rotation angles are all zeros.

  6. Two point sets are congruent if one can be transformed to completely coincide with the other by some rigid transformation.

References

  1. Alt, H., Behrends, B., Blomer, J.: Approximate matching of polygonal shapes (Extended Abstract). In: Proceedings of the 7th ACM Symposium on Computational Geometry (SoCG’91), pp. 186–193 (1991)

  2. Alt, H., Guibas, L.: Discrete geometric shapes: matching, interpolation, and approximation. In: Sack, J.-R., Urrutia, J. (eds.) Handbook of Computational Geometry, pp. 121–153. Elsevier, Amsterdam (1999)

    Google Scholar 

  3. Alt, H., Mehlhorn, K., Wagener, H., Welzl, E.: Congruence, similarity, and symmetries of geometric objects. Discrete Comput. Geom. 3, 237–256 (1988)

    Article  MathSciNet  MATH  Google Scholar 

  4. Andoni, A., Indyk, P., Krauthgamer, R.: Earth mover’s distance over high-dimensional spaces. In: Proccedings of the 19th ACM-SIAM Symposium on Discrete Algorithms (SODA’08), pp. 343–352 (2008)

  5. Andoni, A., Do Ba, K., Indyk, P., Woodruff, D.P.: Efficient sketches for earth mover’s distance, with applications. In: Proccedings 50th IEEE Symposium on Foundations of Computer Science (FOCS’09), pp. 324–330 (2009)

  6. Andoni, A., Onak, K., Nikolov, A., Yaroslavtsev, G.: Parallel Algorithms for Geometric Graph Problems. In: Proccedings of the 46th Symposium on Theory of Computing Conference (STOC’14), pp. 574–583 (2014)

  7. Arun, K.S., Huang, T.S., Blostein, S.D.: Least-squares fitting of two 3-D point sets. IEEE Trans. Pattern Anal. Mach. Intell 9(5), 698–700 (1987)

    Article  Google Scholar 

  8. Arkin, E.M., Kedem, K., Mitchell, J.S.B., Sprinzak, J., Werman, M.: Matching points into pairwise-disjoint noise regions: combinatorial bounds and algorithms. INFORMS J. Comput. 4(4), 375–386 (1992)

    Article  MATH  Google Scholar 

  9. Agarwal, P.K., Phillips, J.M.: On bipartite matching under the RMS distance. In: Proccedings of the 18th Canadian Conference on Computational Geometry (CCCG’06) (2006)

  10. Besl, P.J., McKay, N.D.: A method for registration of 3-D shapes. IEEE Trans. Pattern Anal. Mach. Intell. 14(2), 239–256 (1992)

    Article  Google Scholar 

  11. Benkert, M., Gudmundsson, J., Merrick, D., Wolle, T.: Approximate one-to-one point pattern matching. J. Discrete Algorithms 15, 1–15 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  12. Cohen, S.: Finding color and shape patterns in images. PhD thesis, Stanford University, Department of Compute Science (1999)

  13. Chew, L.P., Dor, D., Efrat, A., Kedem, K.: Geometric pattern matching in d-dimensional space. In: Proccedings of the 3rd European Symposium on Algorithms (ESA’95), pp.  264–279 (1995)

  14. Chew, L.P., Goodrich, M.T., Huttenlocher, D.P., Kedem, K., Kleinberg, J.M., Kravets, D.: Geometric pattern matching under euclidean motion. Comput. Geom. 7, 113–124 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  15. Cabello, S., Giannopoulos, P., Knauer, C.: On the parameterized complexity of d-dimensional point set pattern matching. Inf. Process. Lett. 105(2), 73–77 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  16. Cabello, S., Giannopoulos, P., Knauer, C., Rote, G.: Matching point sets with respect to the earth mover’s distance. Comput. Geom.: Theory Appl. 39(2), 118–133 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  17. Cardoze, D.E., Schulman, L.J.: Pattern matching for spatial point sets. In: Proccedings of the 39th IEEE Symposium on Foundations of Computer Science (FOCS’98), pp. 156–165 (1998)

  18. Clark, C., Kalita, J.: A comparison of algorithms for the pairwise alignment of biological networks. Bioinformatics 30(16), 2351–2359 (2014)

    Article  Google Scholar 

  19. Efrat, A., Itai, A.: Improvements on bottleneck matching and related problems using geometry. In: Proccedings of the 12th ACM Symposium on Computational Geometry (SoCG’96), pp. 301–310 (1996)

  20. Ezra, E., Sharir, M., Efrat, A.: On the performance of the ICP algorithm. Comput. Geom. 41(1–2), 77–93 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  21. Graumann, K., Darell, T.: Fast contour matching using approximate earth mover’s distance. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’04), pp.  220–227 (2004)

  22. Gavrilov, M., Indyk, P., Motwani, R., Venkatasubramanian, S.: Combinatorial and experimental methods for approximate point pattern matching. Algorithmica 38(1), 59–90 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  23. Goodrich, M.T., Mitchell, J.S.B., Orletsky, M.W.: Approximate geometric pattern matching under rigid motions. IEEE Trans. Pattern Anal. Mach. Intell. 21(4), 371–379 (1999)

    Article  Google Scholar 

  24. Giannopoulos, P., Veltkamp, R.: A pseudo-metric for weighted point sets. In: Proccedings of 7th European Conference Computer Vision (ECCV’02), pp. 715–731 (2002)

  25. Huttenlocher, D.P., Kedem, K., Kleinberg, J.M.: On dynamic Voronoi diagrams and the minimum Hausdorff distance for point sets under Euclidean motion in the plane. In: Proccedings of the 8th ACM Symposium on Computational Geometry (SoCG’92), pp. 110–119 (1992)

  26. Indyk, P.: A near linear time constant factor approximation for Euclidean bichromatic matching (cost). In: Proccedings of the 8th ACM-SIAM Symposium on Discrete Algorithms (SODA’07), pp. 39–42 (2007)

  27. Klein, O., Veltkamp, R.C.: Approximation algorithms for computing the earth mover’s distance under transformations. In: Proccedings of the 16th International Symposium on Algorithms and Computation (ISAAC’05), pp. 1019–1028 (2005)

  28. Rubner, Y., Tomasi, C., Guibas, L.J.: The earth mover’s distance as a metric for image retrieval. Int. J. Comput. Vis. 40(2), 99–121 (2000)

    Article  MATH  Google Scholar 

  29. Sharathkumar, R., Agarwal, P. K.: Algorithms for the transportation problem in geometric settings. In: Proccedings of the 23rd ACM-SIAM Symposium on Discrete Algorithms (SODA ’12), pp. 306–317 (2012)

  30. Typke, R., Giannopoulos, P., Veltkamp, R.C., Wierking, F., Oostrum, R.: Using transportation distances for measuring melodic similarity. In: Proccedings of the 4th International Conference Music Information Retrieval, pp. 107–114 (2003)

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hu Ding.

Additional information

This research was supported in part by NSF under Grants CCF-1422324, IIS- 1422591, IIS-1115220, and CNS-1547167. A preliminary version of this paper has appeared in the 21st European Symposium on Algorithms (ESA 2013).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ding, H., Xu, J. FPTAS for Minimizing the Earth Mover’s Distance Under Rigid Transformations and Related Problems. Algorithmica 78, 741–770 (2017). https://doi.org/10.1007/s00453-016-0173-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00453-016-0173-4

Keywords

Navigation