Abstract

Classification decision tree algorithms have recently been used in pattern-recognition problems. In this paper, we propose a self-designing system that uses the classification tree algorithms and that is capable of recognizing a large number of signals. Preprocessing techniques are used to make the recognition process more effective. A combination of the original, as well as the preprocessed, signals is projected into different transform domains. Enormous sets of criteria that characterize the signals can be developed from the signal representations in these domains. At each node of the classification tree, an appropriately selected criterion is optimized with respect to desirable performance features such as complexity and noise immunity. The criterion is then employed in conjunction with a vector quantizer to divide the signals presented at a particular node in that stage into two approximately equal groups. When the process is complete, each signal is represented by a unique composite binary word index, which corresponds to the signal path through the tree, from the input to one of the terminal nodes of the tree. Experimental results verify the excellent classification accuracy of this system. High performance is maintained for both noisy and corrupt data.

© 2004 Optical Society of America

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    [CrossRef] [PubMed]
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  3. W. B. Mikhael, M. M. Abdelwahab, “Multi criteria multi transform neural network,” Circuits Syst. Signal Process. 21, 451–460 (2002).
    [CrossRef]
  4. G. P. Zhang, “Neural networks for classification: a survey,” IEEE Trans. Syst. Man Cybern. 30, 451–462 (2000).
    [CrossRef]
  5. B. Chen, P. K. Varshney, “A Bayesian sampling approach to decision fusion using hierarchical models,” IEEE Trans. Signal Process. 50, 1809–1818 (2002).
    [CrossRef]
  6. M. Simard, S. S. Saatchi, G. De Grandi, “The use of decision tree and multiscale texture for classification of JERS-1 SAR data over tropical forest,” IEEE Trans. Geosci. Remote Sens. 38, 2310–2321 (2000).
    [CrossRef]
  7. M. A. Kupinski, D. C. Edwards, M. L. Giger, C. E. Metz, “Ideal observer approximation using Bayesian classification neural networks,” IEEE Trans. Med. Imaging 20, 886–899 (2001).
    [CrossRef] [PubMed]
  8. A. Srivastava, E. Han, V. Kumar, “Parallel formulations of decision-tree classification algorithms,” in Proceedings of the International Conference on Parallel Processing (Minneapolis, 1998), pp. 237–244.
  9. S. B. Gelfand, C. S. Ravishankar, E. J. Delp, “An iterative growing and pruning algorithm for classification tree design,” IEEE Trans. Pattern Anal. Mach. Intell. 13, 163–174 (1991).
    [CrossRef]
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    [CrossRef]
  11. A. Senior, “A combination fingerprint classifier,” IEEE Trans. Pattern Anal. Mach. Intell. 23, 1165–1174 (2001).
    [CrossRef]
  12. C. E. Brodley, M. A. Friedl, A. H. Strahler, “New approaches to classification in remote sensing using homogeneous and hybrid decision trees to map land cover,” International Symposium for Geoscience and Remote Sensing, Lincoln, Nebr., 1, 532–534 (1996).
  13. P. Tu, J. Chung, “A new decision-tree classification algorithm for machine learning,” in Proceedings of the 1992 IEEE International Conference on Tools with AI (Institute of Electrical and Electronics Engineers, New York, 1992), pp. 370–377.
  14. P. C. Cosman, K. L. Oehler, E. A. Riskin, R. M. Gray, “Using vector quantization for image processing,” Proc. IEEE 81, 1326–1341 (1993).
    [CrossRef]
  15. Y. Gao, M. K. H. Leung, “Face recognition using line edge map,” IEEE Trans. Pattern Anal. Mach. Intell. 24, 764–779 (2002).
    [CrossRef]
  16. P. Meer, B. Georgescu, “Edge detection with embedded confidence,” IEEE Trans. Pattern Anal. Mach. Intell. 23, 1351–1365 (2001).
    [CrossRef]
  17. F. L. Valverde, N. Guil, J. Munoz, R. Nishikawa, K. Doi, “An evaluation criterion for edge detection techniques in noisy images,” in Proceedings of the 2001 IEEE Signal Processing Society International Conference on Image Processing (Institute of Electrical and Electronics Engineers, New York, 2001), pp. 766–769.
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    [CrossRef]
  19. D. F. Eilliott, K. R. Rao, Fast Transforms: Algorithms, Analyses, Applications (Academic, New York, 1982).
  20. J. O. Chapa, R. M. Rao, “Algorithms for designing wavelets to match a specified signal,” IEEE Trans. Signal Process. 48, 3395–3406 (2000).
    [CrossRef]
  21. H. B. Li, J. Licheng, “Segmentation and recognition of bridges in high resolution SAR images,” in 2001 CIE International Conference on Radar Proceedings, W. Shunjun, ed. (Chinese Institute of Electronics, Beijing, China, 2001), pp. 479–482.
  22. J. N. Patel, A. A. Khokhar, L. H. Jamieson, “Scalability of 2-D wavelet transform algorithms: analytical and experimental results on mPPs,” IEEE Trans. Signal Process. 48, 3407–3419 (2000).
    [CrossRef]
  23. T. Chang, C. C. J. Kuo, “Texture analysis and classification with tree-structured wavelet transform,” IEEE Trans. Image Process. 12, 429–441 (1993).
    [CrossRef]
  24. N. Aydin, H. S. Markus, “Directional wavelet transform in the context of complex quadrature Doppler signals,” IEEE Signal Process Lett. 7, 278–280 (2000).
    [CrossRef]
  25. S. K. Sinha, F. Karry, “Classification of underground pipe scanned images using feature extraction and neuro-fuzzy algorithm,” IEEE Trans. Neural Netw. 13, 393–401 (2002).
    [CrossRef]
  26. A. Cohen, B. Matei, “Compact representation of images by edge adapted multiscale transforms,” in Proceedings of International Conference on Image Processing (2001), pp. 8–11.
  27. R. L. Joshi, H. Jafarkhani, J. Kasner, T. Fischer, N. Farvardin, M. W. Marcellin, R. Bamberger, “Comparison of different methods of classification in suband coding of images,” IEEE Trans. Image Process. 6, 1473–1486 (1997).
    [CrossRef]

2002 (4)

W. B. Mikhael, M. M. Abdelwahab, “Multi criteria multi transform neural network,” Circuits Syst. Signal Process. 21, 451–460 (2002).
[CrossRef]

B. Chen, P. K. Varshney, “A Bayesian sampling approach to decision fusion using hierarchical models,” IEEE Trans. Signal Process. 50, 1809–1818 (2002).
[CrossRef]

Y. Gao, M. K. H. Leung, “Face recognition using line edge map,” IEEE Trans. Pattern Anal. Mach. Intell. 24, 764–779 (2002).
[CrossRef]

S. K. Sinha, F. Karry, “Classification of underground pipe scanned images using feature extraction and neuro-fuzzy algorithm,” IEEE Trans. Neural Netw. 13, 393–401 (2002).
[CrossRef]

2001 (5)

B. Verma, J. Zakos, “A computer-aided diagnosis system for digital mammograms based on fuzzy-neural and feature extraction techniques,” IEEE Trans. Inf. Technol. Biomed. 5, 46–54 (2001).
[CrossRef] [PubMed]

P. Meer, B. Georgescu, “Edge detection with embedded confidence,” IEEE Trans. Pattern Anal. Mach. Intell. 23, 1351–1365 (2001).
[CrossRef]

N. C. Rowe, L. L. Grewe, “Change detection for linear features in aerial photographs using edge-finding,” IEEE Trans. Geosci. Remote Sens. 39, 1608–1612 (2001).
[CrossRef]

M. A. Kupinski, D. C. Edwards, M. L. Giger, C. E. Metz, “Ideal observer approximation using Bayesian classification neural networks,” IEEE Trans. Med. Imaging 20, 886–899 (2001).
[CrossRef] [PubMed]

A. Senior, “A combination fingerprint classifier,” IEEE Trans. Pattern Anal. Mach. Intell. 23, 1165–1174 (2001).
[CrossRef]

2000 (5)

M. Simard, S. S. Saatchi, G. De Grandi, “The use of decision tree and multiscale texture for classification of JERS-1 SAR data over tropical forest,” IEEE Trans. Geosci. Remote Sens. 38, 2310–2321 (2000).
[CrossRef]

G. P. Zhang, “Neural networks for classification: a survey,” IEEE Trans. Syst. Man Cybern. 30, 451–462 (2000).
[CrossRef]

J. O. Chapa, R. M. Rao, “Algorithms for designing wavelets to match a specified signal,” IEEE Trans. Signal Process. 48, 3395–3406 (2000).
[CrossRef]

J. N. Patel, A. A. Khokhar, L. H. Jamieson, “Scalability of 2-D wavelet transform algorithms: analytical and experimental results on mPPs,” IEEE Trans. Signal Process. 48, 3407–3419 (2000).
[CrossRef]

N. Aydin, H. S. Markus, “Directional wavelet transform in the context of complex quadrature Doppler signals,” IEEE Signal Process Lett. 7, 278–280 (2000).
[CrossRef]

1997 (1)

R. L. Joshi, H. Jafarkhani, J. Kasner, T. Fischer, N. Farvardin, M. W. Marcellin, R. Bamberger, “Comparison of different methods of classification in suband coding of images,” IEEE Trans. Image Process. 6, 1473–1486 (1997).
[CrossRef]

1993 (2)

T. Chang, C. C. J. Kuo, “Texture analysis and classification with tree-structured wavelet transform,” IEEE Trans. Image Process. 12, 429–441 (1993).
[CrossRef]

P. C. Cosman, K. L. Oehler, E. A. Riskin, R. M. Gray, “Using vector quantization for image processing,” Proc. IEEE 81, 1326–1341 (1993).
[CrossRef]

1991 (1)

S. B. Gelfand, C. S. Ravishankar, E. J. Delp, “An iterative growing and pruning algorithm for classification tree design,” IEEE Trans. Pattern Anal. Mach. Intell. 13, 163–174 (1991).
[CrossRef]

Abdelwahab, M. M.

W. B. Mikhael, M. M. Abdelwahab, “Multi criteria multi transform neural network,” Circuits Syst. Signal Process. 21, 451–460 (2002).
[CrossRef]

M. M. Abdelwahab, W. B. Mikhael, “Neural network pattern recognition employing multicriteria extracted from signal projections in multiple transform domains,” in Proceedings of the International Symposium on Intelligent Multimedia, Video and Speech Processing (Kowloon Shangri-La, Hong Kong, 2001), pp. 40–43.

Atlas, L.

L. Atlas, J. Connor, D. Park, M. El-Sharkawi, R. Marks, A. Lippman, R. Cole, Y. Muthusamy, “A performance comparison of trained multi-layer perceptrons and trained classification tree,” IEEE International Conference on Systems, Man and Cybernetics (Institute of Electrical and Electronics Engineers, New York, 1989), pp. 915–920.
[CrossRef]

Aydin, N.

N. Aydin, H. S. Markus, “Directional wavelet transform in the context of complex quadrature Doppler signals,” IEEE Signal Process Lett. 7, 278–280 (2000).
[CrossRef]

Bamberger, R.

R. L. Joshi, H. Jafarkhani, J. Kasner, T. Fischer, N. Farvardin, M. W. Marcellin, R. Bamberger, “Comparison of different methods of classification in suband coding of images,” IEEE Trans. Image Process. 6, 1473–1486 (1997).
[CrossRef]

Brodley, C. E.

C. E. Brodley, M. A. Friedl, A. H. Strahler, “New approaches to classification in remote sensing using homogeneous and hybrid decision trees to map land cover,” International Symposium for Geoscience and Remote Sensing, Lincoln, Nebr., 1, 532–534 (1996).

Chang, T.

T. Chang, C. C. J. Kuo, “Texture analysis and classification with tree-structured wavelet transform,” IEEE Trans. Image Process. 12, 429–441 (1993).
[CrossRef]

Chapa, J. O.

J. O. Chapa, R. M. Rao, “Algorithms for designing wavelets to match a specified signal,” IEEE Trans. Signal Process. 48, 3395–3406 (2000).
[CrossRef]

Chen, B.

B. Chen, P. K. Varshney, “A Bayesian sampling approach to decision fusion using hierarchical models,” IEEE Trans. Signal Process. 50, 1809–1818 (2002).
[CrossRef]

Chung, J.

P. Tu, J. Chung, “A new decision-tree classification algorithm for machine learning,” in Proceedings of the 1992 IEEE International Conference on Tools with AI (Institute of Electrical and Electronics Engineers, New York, 1992), pp. 370–377.

Cohen, A.

A. Cohen, B. Matei, “Compact representation of images by edge adapted multiscale transforms,” in Proceedings of International Conference on Image Processing (2001), pp. 8–11.

Cole, R.

L. Atlas, J. Connor, D. Park, M. El-Sharkawi, R. Marks, A. Lippman, R. Cole, Y. Muthusamy, “A performance comparison of trained multi-layer perceptrons and trained classification tree,” IEEE International Conference on Systems, Man and Cybernetics (Institute of Electrical and Electronics Engineers, New York, 1989), pp. 915–920.
[CrossRef]

Connor, J.

L. Atlas, J. Connor, D. Park, M. El-Sharkawi, R. Marks, A. Lippman, R. Cole, Y. Muthusamy, “A performance comparison of trained multi-layer perceptrons and trained classification tree,” IEEE International Conference on Systems, Man and Cybernetics (Institute of Electrical and Electronics Engineers, New York, 1989), pp. 915–920.
[CrossRef]

Cosman, P. C.

P. C. Cosman, K. L. Oehler, E. A. Riskin, R. M. Gray, “Using vector quantization for image processing,” Proc. IEEE 81, 1326–1341 (1993).
[CrossRef]

De Grandi, G.

M. Simard, S. S. Saatchi, G. De Grandi, “The use of decision tree and multiscale texture for classification of JERS-1 SAR data over tropical forest,” IEEE Trans. Geosci. Remote Sens. 38, 2310–2321 (2000).
[CrossRef]

Delp, E. J.

S. B. Gelfand, C. S. Ravishankar, E. J. Delp, “An iterative growing and pruning algorithm for classification tree design,” IEEE Trans. Pattern Anal. Mach. Intell. 13, 163–174 (1991).
[CrossRef]

Doi, K.

F. L. Valverde, N. Guil, J. Munoz, R. Nishikawa, K. Doi, “An evaluation criterion for edge detection techniques in noisy images,” in Proceedings of the 2001 IEEE Signal Processing Society International Conference on Image Processing (Institute of Electrical and Electronics Engineers, New York, 2001), pp. 766–769.

Edwards, D. C.

M. A. Kupinski, D. C. Edwards, M. L. Giger, C. E. Metz, “Ideal observer approximation using Bayesian classification neural networks,” IEEE Trans. Med. Imaging 20, 886–899 (2001).
[CrossRef] [PubMed]

Eilliott, D. F.

D. F. Eilliott, K. R. Rao, Fast Transforms: Algorithms, Analyses, Applications (Academic, New York, 1982).

El-Sharkawi, M.

L. Atlas, J. Connor, D. Park, M. El-Sharkawi, R. Marks, A. Lippman, R. Cole, Y. Muthusamy, “A performance comparison of trained multi-layer perceptrons and trained classification tree,” IEEE International Conference on Systems, Man and Cybernetics (Institute of Electrical and Electronics Engineers, New York, 1989), pp. 915–920.
[CrossRef]

Farvardin, N.

R. L. Joshi, H. Jafarkhani, J. Kasner, T. Fischer, N. Farvardin, M. W. Marcellin, R. Bamberger, “Comparison of different methods of classification in suband coding of images,” IEEE Trans. Image Process. 6, 1473–1486 (1997).
[CrossRef]

Fischer, T.

R. L. Joshi, H. Jafarkhani, J. Kasner, T. Fischer, N. Farvardin, M. W. Marcellin, R. Bamberger, “Comparison of different methods of classification in suband coding of images,” IEEE Trans. Image Process. 6, 1473–1486 (1997).
[CrossRef]

Friedl, M. A.

C. E. Brodley, M. A. Friedl, A. H. Strahler, “New approaches to classification in remote sensing using homogeneous and hybrid decision trees to map land cover,” International Symposium for Geoscience and Remote Sensing, Lincoln, Nebr., 1, 532–534 (1996).

Gao, Y.

Y. Gao, M. K. H. Leung, “Face recognition using line edge map,” IEEE Trans. Pattern Anal. Mach. Intell. 24, 764–779 (2002).
[CrossRef]

Gelfand, S. B.

S. B. Gelfand, C. S. Ravishankar, E. J. Delp, “An iterative growing and pruning algorithm for classification tree design,” IEEE Trans. Pattern Anal. Mach. Intell. 13, 163–174 (1991).
[CrossRef]

Georgescu, B.

P. Meer, B. Georgescu, “Edge detection with embedded confidence,” IEEE Trans. Pattern Anal. Mach. Intell. 23, 1351–1365 (2001).
[CrossRef]

Giger, M. L.

M. A. Kupinski, D. C. Edwards, M. L. Giger, C. E. Metz, “Ideal observer approximation using Bayesian classification neural networks,” IEEE Trans. Med. Imaging 20, 886–899 (2001).
[CrossRef] [PubMed]

Gray, R. M.

P. C. Cosman, K. L. Oehler, E. A. Riskin, R. M. Gray, “Using vector quantization for image processing,” Proc. IEEE 81, 1326–1341 (1993).
[CrossRef]

Grewe, L. L.

N. C. Rowe, L. L. Grewe, “Change detection for linear features in aerial photographs using edge-finding,” IEEE Trans. Geosci. Remote Sens. 39, 1608–1612 (2001).
[CrossRef]

Guil, N.

F. L. Valverde, N. Guil, J. Munoz, R. Nishikawa, K. Doi, “An evaluation criterion for edge detection techniques in noisy images,” in Proceedings of the 2001 IEEE Signal Processing Society International Conference on Image Processing (Institute of Electrical and Electronics Engineers, New York, 2001), pp. 766–769.

Han, E.

A. Srivastava, E. Han, V. Kumar, “Parallel formulations of decision-tree classification algorithms,” in Proceedings of the International Conference on Parallel Processing (Minneapolis, 1998), pp. 237–244.

Jafarkhani, H.

R. L. Joshi, H. Jafarkhani, J. Kasner, T. Fischer, N. Farvardin, M. W. Marcellin, R. Bamberger, “Comparison of different methods of classification in suband coding of images,” IEEE Trans. Image Process. 6, 1473–1486 (1997).
[CrossRef]

Jamieson, L. H.

J. N. Patel, A. A. Khokhar, L. H. Jamieson, “Scalability of 2-D wavelet transform algorithms: analytical and experimental results on mPPs,” IEEE Trans. Signal Process. 48, 3407–3419 (2000).
[CrossRef]

Joshi, R. L.

R. L. Joshi, H. Jafarkhani, J. Kasner, T. Fischer, N. Farvardin, M. W. Marcellin, R. Bamberger, “Comparison of different methods of classification in suband coding of images,” IEEE Trans. Image Process. 6, 1473–1486 (1997).
[CrossRef]

Karry, F.

S. K. Sinha, F. Karry, “Classification of underground pipe scanned images using feature extraction and neuro-fuzzy algorithm,” IEEE Trans. Neural Netw. 13, 393–401 (2002).
[CrossRef]

Kasner, J.

R. L. Joshi, H. Jafarkhani, J. Kasner, T. Fischer, N. Farvardin, M. W. Marcellin, R. Bamberger, “Comparison of different methods of classification in suband coding of images,” IEEE Trans. Image Process. 6, 1473–1486 (1997).
[CrossRef]

Khokhar, A. A.

J. N. Patel, A. A. Khokhar, L. H. Jamieson, “Scalability of 2-D wavelet transform algorithms: analytical and experimental results on mPPs,” IEEE Trans. Signal Process. 48, 3407–3419 (2000).
[CrossRef]

Kumar, V.

A. Srivastava, E. Han, V. Kumar, “Parallel formulations of decision-tree classification algorithms,” in Proceedings of the International Conference on Parallel Processing (Minneapolis, 1998), pp. 237–244.

Kuo, C. C. J.

T. Chang, C. C. J. Kuo, “Texture analysis and classification with tree-structured wavelet transform,” IEEE Trans. Image Process. 12, 429–441 (1993).
[CrossRef]

Kupinski, M. A.

M. A. Kupinski, D. C. Edwards, M. L. Giger, C. E. Metz, “Ideal observer approximation using Bayesian classification neural networks,” IEEE Trans. Med. Imaging 20, 886–899 (2001).
[CrossRef] [PubMed]

Leung, M. K. H.

Y. Gao, M. K. H. Leung, “Face recognition using line edge map,” IEEE Trans. Pattern Anal. Mach. Intell. 24, 764–779 (2002).
[CrossRef]

Li, H. B.

H. B. Li, J. Licheng, “Segmentation and recognition of bridges in high resolution SAR images,” in 2001 CIE International Conference on Radar Proceedings, W. Shunjun, ed. (Chinese Institute of Electronics, Beijing, China, 2001), pp. 479–482.

Licheng, J.

H. B. Li, J. Licheng, “Segmentation and recognition of bridges in high resolution SAR images,” in 2001 CIE International Conference on Radar Proceedings, W. Shunjun, ed. (Chinese Institute of Electronics, Beijing, China, 2001), pp. 479–482.

Lippman, A.

L. Atlas, J. Connor, D. Park, M. El-Sharkawi, R. Marks, A. Lippman, R. Cole, Y. Muthusamy, “A performance comparison of trained multi-layer perceptrons and trained classification tree,” IEEE International Conference on Systems, Man and Cybernetics (Institute of Electrical and Electronics Engineers, New York, 1989), pp. 915–920.
[CrossRef]

Marcellin, M. W.

R. L. Joshi, H. Jafarkhani, J. Kasner, T. Fischer, N. Farvardin, M. W. Marcellin, R. Bamberger, “Comparison of different methods of classification in suband coding of images,” IEEE Trans. Image Process. 6, 1473–1486 (1997).
[CrossRef]

Marks, R.

L. Atlas, J. Connor, D. Park, M. El-Sharkawi, R. Marks, A. Lippman, R. Cole, Y. Muthusamy, “A performance comparison of trained multi-layer perceptrons and trained classification tree,” IEEE International Conference on Systems, Man and Cybernetics (Institute of Electrical and Electronics Engineers, New York, 1989), pp. 915–920.
[CrossRef]

Markus, H. S.

N. Aydin, H. S. Markus, “Directional wavelet transform in the context of complex quadrature Doppler signals,” IEEE Signal Process Lett. 7, 278–280 (2000).
[CrossRef]

Matei, B.

A. Cohen, B. Matei, “Compact representation of images by edge adapted multiscale transforms,” in Proceedings of International Conference on Image Processing (2001), pp. 8–11.

Meer, P.

P. Meer, B. Georgescu, “Edge detection with embedded confidence,” IEEE Trans. Pattern Anal. Mach. Intell. 23, 1351–1365 (2001).
[CrossRef]

Metz, C. E.

M. A. Kupinski, D. C. Edwards, M. L. Giger, C. E. Metz, “Ideal observer approximation using Bayesian classification neural networks,” IEEE Trans. Med. Imaging 20, 886–899 (2001).
[CrossRef] [PubMed]

Mikhael, W. B.

W. B. Mikhael, M. M. Abdelwahab, “Multi criteria multi transform neural network,” Circuits Syst. Signal Process. 21, 451–460 (2002).
[CrossRef]

M. M. Abdelwahab, W. B. Mikhael, “Neural network pattern recognition employing multicriteria extracted from signal projections in multiple transform domains,” in Proceedings of the International Symposium on Intelligent Multimedia, Video and Speech Processing (Kowloon Shangri-La, Hong Kong, 2001), pp. 40–43.

Munoz, J.

F. L. Valverde, N. Guil, J. Munoz, R. Nishikawa, K. Doi, “An evaluation criterion for edge detection techniques in noisy images,” in Proceedings of the 2001 IEEE Signal Processing Society International Conference on Image Processing (Institute of Electrical and Electronics Engineers, New York, 2001), pp. 766–769.

Muthusamy, Y.

L. Atlas, J. Connor, D. Park, M. El-Sharkawi, R. Marks, A. Lippman, R. Cole, Y. Muthusamy, “A performance comparison of trained multi-layer perceptrons and trained classification tree,” IEEE International Conference on Systems, Man and Cybernetics (Institute of Electrical and Electronics Engineers, New York, 1989), pp. 915–920.
[CrossRef]

Nishikawa, R.

F. L. Valverde, N. Guil, J. Munoz, R. Nishikawa, K. Doi, “An evaluation criterion for edge detection techniques in noisy images,” in Proceedings of the 2001 IEEE Signal Processing Society International Conference on Image Processing (Institute of Electrical and Electronics Engineers, New York, 2001), pp. 766–769.

Oehler, K. L.

P. C. Cosman, K. L. Oehler, E. A. Riskin, R. M. Gray, “Using vector quantization for image processing,” Proc. IEEE 81, 1326–1341 (1993).
[CrossRef]

Park, D.

L. Atlas, J. Connor, D. Park, M. El-Sharkawi, R. Marks, A. Lippman, R. Cole, Y. Muthusamy, “A performance comparison of trained multi-layer perceptrons and trained classification tree,” IEEE International Conference on Systems, Man and Cybernetics (Institute of Electrical and Electronics Engineers, New York, 1989), pp. 915–920.
[CrossRef]

Patel, J. N.

J. N. Patel, A. A. Khokhar, L. H. Jamieson, “Scalability of 2-D wavelet transform algorithms: analytical and experimental results on mPPs,” IEEE Trans. Signal Process. 48, 3407–3419 (2000).
[CrossRef]

Rao, K. R.

D. F. Eilliott, K. R. Rao, Fast Transforms: Algorithms, Analyses, Applications (Academic, New York, 1982).

Rao, R. M.

J. O. Chapa, R. M. Rao, “Algorithms for designing wavelets to match a specified signal,” IEEE Trans. Signal Process. 48, 3395–3406 (2000).
[CrossRef]

Ravishankar, C. S.

S. B. Gelfand, C. S. Ravishankar, E. J. Delp, “An iterative growing and pruning algorithm for classification tree design,” IEEE Trans. Pattern Anal. Mach. Intell. 13, 163–174 (1991).
[CrossRef]

Riskin, E. A.

P. C. Cosman, K. L. Oehler, E. A. Riskin, R. M. Gray, “Using vector quantization for image processing,” Proc. IEEE 81, 1326–1341 (1993).
[CrossRef]

Rowe, N. C.

N. C. Rowe, L. L. Grewe, “Change detection for linear features in aerial photographs using edge-finding,” IEEE Trans. Geosci. Remote Sens. 39, 1608–1612 (2001).
[CrossRef]

Saatchi, S. S.

M. Simard, S. S. Saatchi, G. De Grandi, “The use of decision tree and multiscale texture for classification of JERS-1 SAR data over tropical forest,” IEEE Trans. Geosci. Remote Sens. 38, 2310–2321 (2000).
[CrossRef]

Senior, A.

A. Senior, “A combination fingerprint classifier,” IEEE Trans. Pattern Anal. Mach. Intell. 23, 1165–1174 (2001).
[CrossRef]

Simard, M.

M. Simard, S. S. Saatchi, G. De Grandi, “The use of decision tree and multiscale texture for classification of JERS-1 SAR data over tropical forest,” IEEE Trans. Geosci. Remote Sens. 38, 2310–2321 (2000).
[CrossRef]

Sinha, S. K.

S. K. Sinha, F. Karry, “Classification of underground pipe scanned images using feature extraction and neuro-fuzzy algorithm,” IEEE Trans. Neural Netw. 13, 393–401 (2002).
[CrossRef]

Srivastava, A.

A. Srivastava, E. Han, V. Kumar, “Parallel formulations of decision-tree classification algorithms,” in Proceedings of the International Conference on Parallel Processing (Minneapolis, 1998), pp. 237–244.

Strahler, A. H.

C. E. Brodley, M. A. Friedl, A. H. Strahler, “New approaches to classification in remote sensing using homogeneous and hybrid decision trees to map land cover,” International Symposium for Geoscience and Remote Sensing, Lincoln, Nebr., 1, 532–534 (1996).

Tu, P.

P. Tu, J. Chung, “A new decision-tree classification algorithm for machine learning,” in Proceedings of the 1992 IEEE International Conference on Tools with AI (Institute of Electrical and Electronics Engineers, New York, 1992), pp. 370–377.

Valverde, F. L.

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Figures (5)

Fig. 1
Fig. 1

Tree structure for recognition of N signals, with one signal identified at each stage.

Fig. 2
Fig. 2

Tree structure for recognition of N signals of unknown probability of occurrence.

Fig. 3
Fig. 3

Proposed pattern-recognition system in the learning mode.

Fig. 4
Fig. 4

Proposed pattern-recognition system in the running mode.

Fig. 5
Fig. 5

Thirty-two facial images downloaded from the Internet.

Equations (11)

Equations on this page are rendered with MathJax. Learn more.

gij=1N,i=0, 0jN-12Ncosπ2j+1i2N,1iN-1, 0jN-1.
walu, v=-1i=0n-1 uivi,
B=1N2WXWT,
g0j=1N, 0jN-1,gij=1N2r/2,m-12rjNm-1/22r-2r/2,m-1/22rjNm2r0,otherwise,
Y=GXGT.
X=USVT,
Xvi=siui.
Wsa, b=1|a|- stψ*t-badt,
gwj=sij,
gwj+1=Sj-sij=Sj+1,
Psi1, Psi2,, PsiN such that Psi1>Psi2>>PsiN.

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