Abstract
This paper presents a new similarity measure for object recognition from large libraries of line-patterns. The measure commences from a Bayesian consistency criterion which as been developed for locating correspondence matches between attributed relational graphs using iterative relaxation operations. The aim in this paper is to simplify the consistency measure so that it may used in a non-iterative manner without the need to compute explicit correspondence matches. This considerably reduces the computational overheads and renders the consistency measure suitable for large-scale object recognition. The measure uses robust error-kernels to gauge the similarity of pairwise attribute relations defined on the edges of nearest neighbour graphs. We use the similarity measure in a recognition experiment which involves a library of over 2000 line-patterns. A sensitivity study reveals that the method is capable of delivering a recognition accuracy of 94%. A comparative study reveals that the method is most effective when a Gaussian kernel or Huber's robust kernel is used to weight the attribute relations. Moreover, the method consistently outperforms Rucklidge's median Hausdorff distance.
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References
Y. Lamdan and H. Wolfson, “Geometric hashing: A general and efficient model-based recognition scheme,” In, Proc. of ICCV, pp. 238–249, 1988.
M. Costa and L. Shapiro, “Scene analysis using appearance-based models and relational indexing,” In Proc. of ISCV, pp. 103–108, 1995.
C. A. Rothwell, A. Zisserman, D. Forsyth, and J. Mundy, “Canonical frames for planar object recognition,” In Proc. of ECCV, pp. 757–772, 1992.
Y. Landam, J. T. Schwartz, and H. J. Wolfson, “Object recognition by affine invariant matching,” in Proc. of CVPR Conf., pp. 335–344, 1988.
A. Evans, N. Thacker, and J. Mayhew, “The use of geometric histograms for model-based object recognition,” In Proc. of BMVC, pp. 429–438, 1993.
W. Rucklidge, “Locating ojects using the Hausdorff distance,” In Proc. of ICCV, pp. 457–464, 1995.
R. Wilson and E. R. Hancock, “Structural matching by discrete relaxation,” PAMI, vol. 19, pp. 634–648, June 1997.
A. Cross and E. Hancock, “Recovering perspective pose with dual step EM algorithm,” Advances in NIPS, vol. 10, 1998. MIT Press, to appear.
B. Huet and E. R. Hancock, “Cartographic indexing into a database of remotely sensed images,” In Proc. of WACV, pp. 8–14, Dee 1996.
J. Kittler and E. Hancock, “Combining evidence in probabilistic relaxation,” Int. J. of Pattern Recognition and Artificial Intelligence, vol. 3, no. 1, pp. 29–51, 1989.
W. Christmas, J. Kittler, and M. Petrou, “Structural matching in computer vision using probabilistic relaxation,” PAMI, vol. 17, pp. 749–764, 1995.
A. Pentland, R. Picard, and S. Sclaroff, “Photobook: Content-based manipulation of image databases,” Int. J. of Computer Vision, vol. 18, no. 3, pp. 233–254, 1996.
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© 1998 Springer-Verlag Berlin Heidelberg
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Huet, B., Hancock, E.R. (1998). Object recognition from large structural libraries. In: Amin, A., Dori, D., Pudil, P., Freeman, H. (eds) Advances in Pattern Recognition. SSPR /SPR 1998. Lecture Notes in Computer Science, vol 1451. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0033237
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DOI: https://doi.org/10.1007/BFb0033237
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