Abstract
This paper presents a method of matching ambiguous feature sets extracted from images. The method is based on Wilson and Hancock’s Bayesian matching framework [1], which is extended to handle the case where the feature measurements are ambiguous. A multimodal evolutionary optimisation framework is proposed, which is capable of simultaneously producing several good alternative solutions. Unlike other multimodal genetic algorithms, the one reported here requires no extra parameters: solution yields are maximised by removing bias in the selection step, while optimisation performance is maintained by a local search step. An experimental study demonstrates the effectiveness of the new approach on synthetic and real data. The framework is in principle applicable to any multimodal optimisation problem where local search performs well.
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References
R. C. Wilson and E. R. Hancock. Structural matching by discrete relaxation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19:634–648, 1997.
H. G. Barrow and R. J. Popplestone. Relational descriptions in picture processing. In B. Meltzer and D. Michie, editors, Machine Intelligence, volume 6. Edinburgh University Press, 1971.
M. Minsky. A framework for representing knowledge. In P.H. Winston, editor, The Psychology of Computer Vision. McGraw-Hill, 1975.
J. Koenderink and A. van Doorn. The internal representation of solid shape with respect to vision. Biological Cybernetics, 32:211–216, 1979.
S. J. Dickinson, A. P. Pentland, and A. Rosenfeld. 3-D shape recovery using distributed aspect matching. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14:174–198, 1992.
R. Horaud and T. Skordas. Stereo correspondence through feature grouping and maximal cliques. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11:1168–1180, 1989.
A. C. M. Dumay, R. J. van der Geest, J. J. Gerbrands, E. Jansen, and J. H. C. Reiber. Consistent inexact graph matching applied to labeling coronary segments in arteriograms. In Proceedings of the 11th International Conference on Pattern Recognition, volume C, pages 439–442, 1992.
A. Sanfeliu and K. S. Fu. A distance measure between attributed relational graphs for pattern recognition. IEEE Transactions on Systems, Man and Cybernetics, 13:353–362, 1983.
L. G. Shaprio and R. M. Haralick. A metric for comparing relational descriptions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 7:90–94, 1985.
A. K. C. Wong and M. You. Entropy and distance of random graphs with application to structural pattern recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 7:599–609, 1985.
J. Liang, H. I. Christensen, and F. V. Jensen. Qualitative recognition using Bayesian reasoning. In E. S. Gelsema and L. S. Kanal, editors, Pattern Recognition in Practice IV, pages 255–266, 1994.
K. W. Boyer and A. C. Kak. Structural stereopsis for 3-D vision. IEEE Transactions on Pattern Analysis and Machine Intelligence, 10:144–166, 1988.
J. Kittler, W. J. Christmas, and M. Petrou. Probabilistic relaxation for matching problems in computer vision. In Proceedings of the 4th IEEE International Conference on Computer Vision, pages 666–673, 1993.
R. C. Wilson and E. R. Hancock. A Bayesian compatibility model for graph matching. Pattern Recognition Letters, 17:263–276, 1996.
R. C. Wilson and E. R. Hancock. Graph matching by discrete relaxation. In E. S. Gelsema and L. N. Kanal, editors, Pattern Recognition in Practice, volume 4, pages 165–176. Elsevier, 1994.
A. D. J. Cross, R. C. Wilson, and E. R. Hancock. Inexact graph matching using genetic search. Pattern Recognition, 30:953–970, 1997.
D. Beasley, D. R. Bull, and R. R. Martin. A sequential niche technique for multimodal function optimisation. Evolutionary Computation, 1:101–125, 1993.
J. H. Holland. Adaptation in Natural and Artificial Systems. MIT Press, 1975.
D. Goldberg. Genetic Algorithms in Search, Optimisation and Machine Learning. Addison-Wesley, 1989.
D. E. Goldberg and J. Richardson. Genetic algorithms with sharing for multimodal function optimization. In Proceedings of the 2nd International Conference on Genetic Algorithms, pages 41–49, 1987.
W. Cedeño, V. R. Vemuri, and T. Slezak. Multiniche crowding in genetic algorithms and its application to the assembly of DNA restriction-fragments. Evolutionary Computation, 2:321–345, 1995.
M. Gorges-Schleuter. ASPARAGOS: A parallel genetic algorithm for population genetics. In Parallelism, Learning, Evolution. Workshop on Evolutionary Models and Strategies, pages 407–418, 1991.
R. E. Smith, S. Forrest, and A. S. Perelson. Searching for diverse, cooperative populations with genetic algorithms. Evolutionary Computation, 1:127–149, 1993.
R. Myers and E. R. Hancock. Genetic algorithms for ambiguous labelling problems. Lecture Notes in Computer Science (EMMCVPR 97), 1223:345–360, 1997. Extended version to appear in Pattern Recognition.
D. Marr. Vision. Freeman, 1982.
K. S. Fu. A step towards unification of syntactic and statistical pattern recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 5:200–205, 1983.
E. R. Hancock and J. Kittler. Discrete relaxation. Pattern Recognition, 23:711–733, 1990.
R. Myers, R. C. Wilson, and E. R. Hancock. Efficient relational matching with local edit distance. In Proceedings of the 14th International Conference on Pattern Recognition, pages 1711–1714, 1998.
V. Levenshtein. Binary codes capable of correcting deletions, insertions, and reversals. Soviet Physics-Doklady, 10:707–710, 1966.
R. A. Hummel and S. W. Zucker. On the foundations of relaxation labeling processes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 5:267–287, 1983.
O. D. Faugeras and M. Berthod. Improving consistency and reducing ambiguity in stochastic labeling: An optimisation approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, 3:412–424, 1981.
G. Syswerda. Uniform crossover in genetic algorithms. In Proceedings of the 3rd International Conference on Genetic Algorithms, pages 2–9, 1989.
J. E. Baker. Reducing bias and inefficiency in the selection algorithm. In Proceedings of the 2nd International Conference on Genetic Algorithms, pages 14–21, 1987.
M. Mitchell. An Introduction to Genetic Algorithms. MIT Press, 1996.
J. E. Baker. Adaptive selection methods for genetic algorithms. In J. J. Grefenstette, editor, Proceedings of the 1st International Conference on Genetic Algorithms, 1985.
D. E. Goldberg. A note on Boltzmann tournament selection for genetic algorithms and population-based simulated annealing. Complex Systems, 4:445–460, 1990.
A. Prügel-Bennett and J. L. Shapiro. An analysis of genetic algorithms using statistical physics. Physical Review Letters, 72:1305–1309, 1994.
I. Rechenberg. Evolutionsstrategie-Optimierung Technischer Systeme nach Prinzipien der biologischen Information. Fromman Verlag, 1973.
H-P. Schwefel. Numerical Optimization of Computer Models. Wiley, 1981.
G. Rudolph. Convergence analysis of canonical genetic algorithms. IEEE Transactions on Neural Networks, 5:96–101, 1994.
L. J. Eshelman. The CHC adaptive search algorithm: How to have safe search when engaging in nontraditional genetic recombination. In G. J. E. Rawlins, editor, Foundations of Genetic Algorithms, volume 1, pages 265–283. Morgan Kaufmann, 1991.
J. R. Shewchuk. Triangle: Engineering a 2D quality mesh generator and Delaunay triangulator. In Proceedings of the 1 st Workshop on Applied Computational Geometry, pages 124–133, 1996.
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Myers, R., Hancock, E.R. (2000). Least Committment Graph Matching by Evolutionary Optimisation. In: Computer Vision - ECCV 2000. ECCV 2000. Lecture Notes in Computer Science, vol 1842. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45054-8_14
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DOI: https://doi.org/10.1007/3-540-45054-8_14
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