CLASSIFICATION OF DEFORMABLE GEOMETRIC SHAPES - Using Radial-Basis Function Networks and Ring-wedge Energy Features

El-Sayed M. El-Alfy

Abstract

This paper describes a system for automatic classification of geometric shapes based on radial-basis function (RBF) neural networks even in the existence of shape deformation. The RBF network model is built using ring-wedge energy features extracted from the Fourier transform of the spatial images of geometric shapes. Using a benchmark dataset, we empirically evaluated and compared the performance of the proposed approach with two other standard classifiers: multi-layer perceptron neural networks and decision trees. The adopted dataset has four geometric shapes (ellipse, triangle, quadrilateral, and pentagon) which may have deformations including rotation, scaling and translation. The empirical results showed that the proposed approach significantly outperforms the other two classification methods with classification error rate around 3.75% on the testing dataset using 5-fold stratified cross validation.

References

  1. Barutcuoglu, Z., DeCoro, C., 2006. Hierarchical shape classification using Bayesian aggregation. In Proceedings of the IEEE International Conference on Shape Modeling and Applications, Matsushima, Japan.
  2. Bishop, C. M., 1995. Neural networks for pattern recognition, Oxford University Press, New York.
  3. Chang, C. C., Hwang, S. M., Buehrer, D. J., 1991. A shape recognition scheme based on relative distances of feature points from the centroid. Pattern Recognition, 24(11): 1053-1063.
  4. Chen, S., Hong X., Harris, C. J., 2005. Orthogonal forward selection for constructing the radial basis function network with tunable nodes. In Proceedings of the IEEE International Conference on Intelligent Computing.
  5. Chen, L., Feris, R. S., Turk, M., 2008. Efficient partial shape matching using Smith-Waterman algorithm. In Workshop on Non-Rigid Shape Analysis and Deformable Image Alignment (NORDIA'08), in conjunction with CVPR'08, Anchorage, Alaska.
  6. Chen, L., McAuley, J., Feris, R., Caetano, T., Turk, M., 2009. Shape classification through structured learning of matching measures. In Proceeding of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2009), Miami, Florida.
  7. Costa, L., Cesar Jr., R. M., 2000. Shape analysis and classification: Theory and practice, CRC Press.
  8. El-Alfy, E.-S. M., 2008. Abductive learning approach for geometric shape recognition. In Proceedings of the International Conference on Intelligent Systems and Exhibition, Bahrain.
  9. Lazzerini, B., Marceelloni, F., 2001. A fuzzy approach to 2-D shape recognition. IEEE Transactions on Fuzzy Systems, 9(1): 5-16.
  10. George, N., Wang, S., Venable, D. L., 1989. Pattern recognition using the ring-wedge detector and neural network software. SPIE, 1134: 96-106.
  11. Gorelick, L., Galun, M., Sharon, E., Basri, R., Brandt, A., 2006. Shape representation and classification using the Poisson equation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(12): 1991- 2005.
  12. Haykin, S., 2009. Neural networks and learning machines. Third Edition, Prentice-Hall.
  13. Ling, H., Jacobs, D. W., 2007. Shape classification using the inner-distance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(2): 286-299.
  14. McNeil, G., Vijayakumar, S., 2005. 2D shape classification and retrieval. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI'05), Edinburgh, Scotland.
  15. Moorehead, L. B., Jones, R. A., 1988. A neural network for shape recognition. In Proceedings of the IEEE Region 5 Conference, Piscataway, NJ.
  16. Neruda, R., Kudova, P., 2005. Learning methods for radial basis functions networks. Future Generation Computer Systems, 21: 1131-1142.
  17. Powel, M., 1985. Radial-basis functions for multivariable interpolation: A review. In Proceedings of the IMA
  18. Pun, C.-M., Lin, C., 2010. Geometric invariant shape classification using hidden Markov model. In Proceedings of the IEEE International Conference on Digital Image Computing: Techniques and Applications (DICTA 2010).
  19. Sherrod, P. H., 2011. DTREG: Predictive modeling software. http://www.dtreg.com/DTREG.pdf
  20. Super, B. J., 2004. Fast correspondence-based system for shape retrieval. Pattern Recognition Letters, 25: 217- 225.
  21. Tsai, A., Wells, W. M., Warfield, S. K., Willsky, A. S., 2005. An EM algorithm for shape classification based on level sets. Medical Image Analysis, 9: 491-502.
  22. Yau, H-C., 1990. Transform-based shape recognition employing neural networks. Ph.D. Dissertation, University of Texas at Arlington.
  23. Yau, H.-C., Manry, M. T., 1991. Shape recognition with nearest neighbor isomorphic network. In Proceedings of the IEEE-SP Workshop on Neural Networks for Signal Processing, Princeton, NJ.
  24. Yau, H.-C., Manry, M. T., 1991. Iterative improvement of a nearest neighbor classifier. Neural Networks, 4: 517- 524.
  25. Zhang, J., Zhang, X., Krim, H., Walter, G. G., 2003. Object representation and recognition in shape spaces. Pattern Recognition, 36: 1143-1154.
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Paper Citation


in Harvard Style

M. El-Alfy E. (2012). CLASSIFICATION OF DEFORMABLE GEOMETRIC SHAPES - Using Radial-Basis Function Networks and Ring-wedge Energy Features . In Proceedings of the 4th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-8425-95-9, pages 355-362. DOI: 10.5220/0003750603550362


in Bibtex Style

@conference{icaart12,
author={El-Sayed M. El-Alfy},
title={CLASSIFICATION OF DEFORMABLE GEOMETRIC SHAPES - Using Radial-Basis Function Networks and Ring-wedge Energy Features},
booktitle={Proceedings of the 4th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2012},
pages={355-362},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003750603550362},
isbn={978-989-8425-95-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 4th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - CLASSIFICATION OF DEFORMABLE GEOMETRIC SHAPES - Using Radial-Basis Function Networks and Ring-wedge Energy Features
SN - 978-989-8425-95-9
AU - M. El-Alfy E.
PY - 2012
SP - 355
EP - 362
DO - 10.5220/0003750603550362