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Optics Express

Optics Express

  • Editor: C. Martijn de Sterke
  • Vol. 17, Iss. 9 — Apr. 27, 2009
  • pp: 7407–7418

The effectiveness of detector combinations

Zhenghao Li, Weiguo Gong, A.Y.C. Nee, and S.K. Ong  »View Author Affiliations


Optics Express, Vol. 17, Issue 9, pp. 7407-7418 (2009)
http://dx.doi.org/10.1364/OE.17.007407


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Abstract

In this paper, the performance improvement benefiting from the combination of local feature detectors for image matching and registration is evaluated. Possible combinations of five types of representative interest point detectors and region detectors are integrated into the testing framework. The performance is compared using the number of correspondences and the repeatability rate, as well as an original evaluation criterion named the Reconstruction Similarity (RS), which reflects not only the number of matches, but also the degree of matching error. It is observed that the combination of DoG extremum and MSCR outperforms any single detectors and other detector combinations in most cases. Furthermore, MDSS, a hybrid algorithm for accurate image matching, is proposed. Compared with standard SIFT and GLOH, its average RS rate exceeds more than 3.56%, and takes up even less computational time.

© 2009 OSA

OCIS Codes
(100.2960) Image processing : Image analysis
(100.5010) Image processing : Pattern recognition
(100.5760) Image processing : Rotation-invariant pattern recognition

ToC Category:
Image Processing

History
Original Manuscript: December 9, 2008
Revised Manuscript: March 20, 2009
Manuscript Accepted: April 18, 2009
Published: April 21, 2009

Citation
Zhenghao Li, Weiguo Gong, A.Y.C. Nee, and S.K. Ong, "The effectiveness of detector combinations," Opt. Express 17, 7407-7418 (2009)
http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-17-9-7407


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References

  1. K. Mikolajczyk and C. Schmid, “Scale & affine invariant interest point detectors,” Int. J. Comput. Vis. 60(1), 63–86 (2004). [CrossRef]
  2. K. Mikolajczyk, T. Tuytelaars, C. Schmid, A. Zisserman, J. Matas, F. Schaffalitzky, T. Kadir, and L. Vangool, “Comparison of affine region detectors,” Int. J. Comput. Vis. 65(1-2), 43–72 (2005). [CrossRef]
  3. K. Mikolajczyk and C. Schmid, “Performance evaluation of local descriptors,” IEEE Trans. Pattern Anal. Mach. Intell. 27(10), 1615–1630 (2005). [CrossRef] [PubMed]
  4. S. A. J. Winder, and M. Brown, “Learning local image descriptors,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Minneapolis, United States, 2007), pp. 17–24.
  5. D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” Int. J. Comput. Vis. 60(2), 91–110 (2004). [CrossRef]
  6. J. Matas, O. Chuma, M. Urbana, and T. Pajdla, “Wide-baseline stereo from maximally stable extremal regions,” Image Vis. Comput. 22(10), 761–767 (2004). [CrossRef]
  7. M. Donoser, and H. Bischof, “Efficient maximally stable extremal region (MSER) tracking,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (New York, United States, 2006), pp. 553–560.
  8. P. E. Forssén, “Maximally stable colour regions for recognition and matching,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Minneapolis, United States, 2007), pp. 1220–1227.
  9. L. Trujillo, G. Olague, P. Legrand, and E. Lutton, “Regularity based descriptor computed from local image oscillations,” Opt. Express 15(10), 6140–6145 (2007). [CrossRef] [PubMed]
  10. T. Lindeberg, “Feature detection with automatic scale selection,” Int. J. Comput. Vis. 30(2), 79–116 (1998). [CrossRef]
  11. T. Tuytelaars and L. Van Gool, “Matching widely separated views based on affine invariant regions,” Int. J. Comput. Vis. 59(1), 61–85 (2004). [CrossRef]
  12. T. Kadir, A. Zisserman, and M. Brady, “An affine invariant salient region detector,” In Proceedings of the European Conference on Computer Vision (Prague, Czech Republic, 2004), pp. 345–457.
  13. W. J. Schroeder, J. A. Zarge, and W. E. Lorensen, “Decimation of triangle meshes,” Comput. Graph. 26(2), 65–70 (1992). [CrossRef]
  14. J. S. Beis, and D. G. Lowe, “Shape indexing using approximate nearest-neighbour search in high-dimensional spaces,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, (San Juan, Puerto Rico, 1997), pp. 1000–1006.
  15. Visual Geometry Group, “Affine covariant regions datasets,” http://www.robots.ox.ac.uk/~vgg/data/ .
  16. A. Bowyer, “Computing Dirichlet tessellations,” Comput. J. 24(2), 162–166 (1981). [CrossRef]
  17. H. Øyvind, and D. Morten, “Triangulations and applications,” in Mathematics and Visualization, G. Farin, H. C. Hege, D. Hoffman, C. R. Johnson, K. Polthier, and M. Rumpf, eds. (Springer, Berlin, 2006).
  18. J. Yang, D. Zhang, A. F. Frangi, and J. Y. Yang, “Two-dimensional PCA: a new approach to appearance-based face representation and recognition,” IEEE Trans. Pattern Anal. Mach. Intell. 26(1), 131–137 (2004). [CrossRef] [PubMed]
  19. Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Process. 13(4), 600–612 (2004). [CrossRef] [PubMed]
  20. J. J. Corso, and G. D. Hager, “Coherent regions for concise and stable image description,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (San Diego, United States, 2005), pp. 184–190.
  21. V. Lepetit and P. Fua, “Keypoint recognition using randomized trees,” IEEE Trans. Pattern Anal. Mach. Intell. 28(9), 1465–1479 (2006). [CrossRef] [PubMed]
  22. V. Lepetit, and P. Fua, “Towards recognizing feature points using classification trees,” in Technical Report IC/2004/74. (EPFL, 2004).
  23. E. Rosten, and T. Drummond, “Fusing points and lines for high performance tracking,” in Proceedings of the IEEE International Conference on Computer Vision (Beijing, China, 2005), pp. 1508–1515.
  24. E. Rosten, and T. Drummond, “Machine learning for high-speed corner detection” in Proceedings of the European Conference on Computer Vision (Graz, Austria, 2006), pp. 430–443.
  25. H. Bay, T. Tuytelaars, and L. Van Gool, “SURF: speeded up robust features,” in Proceedings of the European Conference on Computer Vision (Graz, Austria, 2006), pp. 404–417.
  26. H. Bay, A. Ess, T. Tuytelaars, and L. Van Gool, “Speeded-up robust features (SURF),” Comput. Vis. Image Underst. 110(3), 346–359 (2008). [CrossRef]
  27. T. Liu, A. W. Moore, A. Gray, and K. Yang, “An investigation of practical approximate nearest neighbor algorithms,” in Advances in Neural Information Processing Systems, L. K. Saul, Y. Weiss, and L. Bottou, eds. (MIT Press, Cambridge, 2005). [PubMed]
  28. Z. Li, A. Y. C. Nee, S. K. Ong, and W. Gong, “Tampered image detection using image matching,” in Proceedings of the IEEE Computer Society Conference on Computer Graphics, Imaging and Visualization, (Penang, Malaysia, 2008), pp. 174–179.

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