Skip to main content
SpringerLink
Log in

A novel clique formulation for the visual feature matching problem

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

This paper presents CCMM (acronym for image Clique Matching), a new deterministic algorithm for the visual feature matching problem when images have low distortion. CCMM is multi-hypothesis, i.e. for each feature to be matched in the original image it builds an association graph which captures pairwise compatibility with a subset of candidate features in the target image. It then solves optimum joint compatibility by searching for a maximum clique. CCMM is shown to be more robust than traditional RANSAC-based single-hypothesis approaches. Moreover, the order of the graph grows linearly with the number of hypothesis, which keeps computational requirements bounded for real life applications such as UAV image mosaicing or digital terrain model extraction. The paper also includes extensive empirical validation.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Ballard DH, Brown M (1982) Computer vision. Prentice-Hall, New York

    Google Scholar 

  2. Karp RM (1972) Reducibility among combinatorial problems. In: Miller RE, Thatcher JW (eds), New York, pp 85–103

  3. Tomita E, Seki T (2003) An efficient branch and bound algorithm for finding a maximum clique. In: Calude C, Dinneen M, Vajnovszki V (eds) Discrete mathematics and theoretical computer science, vol 2731. LNCS, pp 278–289

  4. Tomita E, Sutani Y, Higashi T, Takahashi S, Wakatsuki M (2010) A simple and faster branch-and-bound algorithm for finding a maximum clique. Lect Notes Comput Sci 5942:191– 203

    Article  Google Scholar 

  5. Konc J, Janečič D (2007) An improved branch and bound algorithm for the maximum clique problem. MATCH Commun Math Comput Chem 58:569–590

    MathSciNet  MATH  Google Scholar 

  6. San Segundo P, Rodriguez-Losada D, Jimenez A (2011) An exact bit-parallel algorithm for the maximum clique problem. Comput Oper Res 38(2):571–581

    Article  MathSciNet  MATH  Google Scholar 

  7. San Segundo P, Matia F, Rodriguez-Losada D, Hernando M (2011) An improved bit parallel exact maximum clique algorithm. Optimization Letters 7(3):467–479

    Article  MathSciNet  Google Scholar 

  8. Li CM, Quan Z (2010) An efficient branch-and-bound algorithm based on MaxSAT for the maximum clique problem. AAAI, pp 128–133

  9. Li CM, Fang Z, Xu K (2013) Combining MaxSAT reasoning and incremental upper bound for the maximum clique problem. In: 2013 IEEE 25th international conference on tools with articial intelligence (ICTAI), pp 939–946

  10. Batsyn M, Goldengorin B, Maslov E, Pardalos P (2014) Improvements to MCS algorithm for the maximum clique problem. J Comb Optim 27(2):397–416

    Article  MathSciNet  MATH  Google Scholar 

  11. San Segundo P, Tapia C (2014) Relaxed approximate coloring in exact maximum clique search. Comput Oper Res 44:185–192

    Article  MathSciNet  MATH  Google Scholar 

  12. Prosser P (2012) Exact algorithms for maximum clique: a computational study. Algorithms 5(4):545–587

    Article  MathSciNet  MATH  Google Scholar 

  13. Pullan W, Hoos HH (2006) Dynamic local search for the maximum clique problem. J Artif Int Res 25(1):159–185

    MATH  Google Scholar 

  14. Andrade DV, Resende MGC, Werneck RF (2012) Fast local search for the maximum independent set problem. J Heuristics 18(4):525–547

    Article  Google Scholar 

  15. Wu Q, Hao JK (2013) An adaptive multistart tabu search approach to solve the maximum clique problem. J Comb Optim 26(1):86–108

    Article  MathSciNet  MATH  Google Scholar 

  16. Butenko S, Wilhelm WE (2006) Clique-detection models in computational biochemistry and genomics. Eur J Oper Res 173:1–17

    Article  MathSciNet  MATH  Google Scholar 

  17. Hotta K, Tomita E, Takahashi H (2003) Aview invariant human FACE detection method based on maximum cliques. Trans IPSJ 44:57–70. SIG14 (TOM9)

    MATH  Google Scholar 

  18. San Segundo P, Rodriguez-Losada D, Matia F, Galan R (2010) Fast exact feature based data correspondence search with an efficient bit-parallel MCP solver. Appl Intell 32(3):311– 329

    Article  Google Scholar 

  19. San Segundo P, Rodriguez-Losada D (2013) Robust global feature based data association with a sparse bit optimized maximum clique algorithm. IEEE Trans Robot 29(5):1332–1339

    Article  MATH  Google Scholar 

  20. Matula DW (1983) Smallest-Last ordering and clustering and graph coloring algorithms. J Assoc Comput Mach 30(3):417– 427

    Article  MathSciNet  MATH  Google Scholar 

  21. BBMC 1.0. http://intelligentcontrol.disam.etsii.upm.es/arabot/sites/default/files/frontpage

  22. Juan L, Gwun O (2009) A comparison of sift, pca-sift and surf. Int J Image Process (IJIP) 3(4):143–152

    Google Scholar 

  23. Zhang W, Kosecka J (2006) Generalized ransac framework for relaxed correspondence problems. In: Third international symposium on 3D data processing, visualization, and transmission. IEEE, pp 854–860

  24. Huber PJ (1981) Robust statistics. Wiley

  25. Rousseeuw PJ (1984) Least median of squares regression. J Am Stat Assoc 79:871–880

    Article  MathSciNet  Google Scholar 

  26. Fischler MA, Bolles RC (1981) Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM 24(6):381–395

    Article  MathSciNet  Google Scholar 

  27. Lowe D (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110

    Article  Google Scholar 

  28. Choi S, Kim T, Yu W (1997) Performance evaluation of RANSAC family. J Comput Vis 24(3):271–300

    Article  Google Scholar 

  29. Lacey AJ, Pinitkarn N, Thacker AN (2000) An evaluation of the performance of RANSAC algorithms for stereo camera calibrarion. BMVC

  30. Hartley R, Zisserman A (2003) Multiple view geometry in computer vision. Cambridge University Press

  31. Harris C, Stephens M (1988) A combined corner and edge detector. Proc 4th Alvey Vision Conference 15:147–151

    Google Scholar 

  32. Bay H, Tuytelaars T, Van Gool L (2006) Surf: speeded up robust features. Computer Vision–ECCV, pp 404–417

  33. Gruen A (1985) Adaptive least squares correlation: a powerful image matching technique. S Afr J Photogr, Remote Sens Cartogr 14(3):175–187

    Google Scholar 

  34. Friedman JH, Bentley JL, Finkel R (1977) An algorithm for finding best matches in logarithmic expected time. ACM Trans Math Softw (TOMS) 3(3):209–226

    Article  MATH  Google Scholar 

  35. Torr P, Zisserman A (1997) Robust parameterization and computation of the trifocal tensor. Image Vis Comput 15(8):591–605

    Article  Google Scholar 

  36. Armstrong M, Zisserman A, Hartley R (1996) Self-calibration from image triplets. European Conference on Computer Vision (ECCV’96), pp 1–16

  37. Beardsley P, Torr P, Zisserman A (1996) 3D model acquisition from extended image sequences. Computer Vision— European Conference on Computer Vision ECCV ’96, pp 683–695

  38. Triggs B, McLauchlan PF, Hartley R, Fitzgibbon A (2000) Bundle adjustment—A modern synthesis. Vision Algorithms: Theory and Practice, pp 298–372

  39. Torr P, Zisserman A (2000) MLESAC: a new robust estimator with application to estimating image geometry. Comp Vision Image Underst (CVIU) 78 (1):138–156

    Article  Google Scholar 

  40. Fraundorfer F, Scaramuzza D (2012) Visual odometry: Part II: Matching, robustness, optimization, and applications. IEEE Robot Autom Mag 19(2):78–90

    Article  Google Scholar 

  41. Lourakis MIA, Argyros AA (2009) SBA: a software package for generic sparse bundle adjustment. ACM Trans Math Software 36(1):1–30

    Article  MathSciNet  Google Scholar 

  42. Muja M, Lowe DG (2009) Fast approximate nearest neighbors with automatic algorithm configuration. In: International conference on computer vision theory and applications, VISAPP, vol 1, pp 331–340

  43. Yan K, Sukthankar R (2004) PCA-SIFT: a more distinctive representation for local image descriptors. Computer Vision and Pattern Recognition, 2

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

    Article  Google Scholar 

  45. Heligrande, Reduced and Skycam dataset. http://venus.elai.upm.es/datasets/

  46. Alberta University Radish dataset. http://cres.usc.edu/radishrepository/view-one.php?name=ualberta-csc-flr3-vision

  47. University of Oxford Visual Geometry Group datasets. http://www.robots.ox.ac.uk/~vgg/data1.html

Download references

Acknowledgments

This work is funded by the Spanish Ministry of Economy and Competitiveness (ARABOT: DPI 2010-21247-C02-01) and supervised by CACSA whose kindness we gratefully acknowledge.

Author information

Authors and Affiliations

  1. Center for Automation and Robotics (UPM-CSIC), C/ Jose Gutiérrez Abascal, 2, 28006, Madrid, Spain

    Pablo San Segundo & Jorge Artieda

Authors
  1. Pablo San Segundo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jorge Artieda
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pablo San Segundo.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

San Segundo, P., Artieda, J. A novel clique formulation for the visual feature matching problem. Appl Intell 43, 325–342 (2015). https://doi.org/10.1007/s10489-015-0646-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-015-0646-1

Keywords

Search

Navigation