Abstract
To recognize the handwritten digit, this paper proposes a hierarchical Gabor features extraction method using a hierarchical Gabor filter scheme and hierarchical structure encoding the dependencies among the hierarchical Gabor features for a hierarchical bayesian network(HBN). Hierarchical Gabor features represent a different level of information which is structured such that, the higher the level, the more global information they represent, and the lower the level, the more localized information they represent. This is accomplished by a hierarchical Gabor filter scheme. HBN is a statistical model whose joint probability represents dependencies among the features hierarchically. A fully connected HBN may include irrelevant information which is useless for recognition. Pruning method can remove this irrelevant information so that the complexity of HBN can be reduced and the recognition can be accomplished more efficiently. In the experiments, we show the results of handwritten digit recognition by HBN with the hierarchical Gabor features and we compare with the naive bayesian classifier, the back-propagation neural network and the k-nearest neighbor classifier. Our proposed HBN outperforms all these methods in the experiments.
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References
Tai Sing Lee.: Image Representation Using 2D GaborWavelets. IEEE trans. PAMI, vol. 18. no. 10. (1996) 959–971
Pearl, J.: Probabilistic Inference in Intelligent Systems. Morgan Kaufmann, San Mateo, California, (1988)
Robert G. Cowell, A. Philip Dawid, Steffen L. Lauritzen, David J, Spiegelhalter.: Probabilistic Networks and Expert Systems. Springer (1999)
Heckerman, D.: A tutorial on learning with Bayesian networks. In ‘Learning in Graphical Models’. (ed. M. I. Jordan). Kluwer Academic Publishers, Dordrecht, The Netherlands, (1998) 301–354
N. Friedman, D. Geiger, M Goldszmidt.: Bayesian network classifiers. Mach. Learn 29 (1997) 131–163
C. L. Blake and C. J. Merz.: UCI Repository of Machine Learning Databases. Dept. of Information and Computer Science, Uive. of California, Irvine, (1998)
Chistopher M. Bishop.: Neural Networks for Pattern Recognition. Oxford University Press (1995)
Richard O. Duda, Peter E. Hart, David G. Strok.: Pattern Classification. John Wily and Sons. (2001)
Daijin Kim, Sung-Yang Bang.: A Handwritten Numeral Character Classification Using Tolerant Rough Set. IEEE trans. PAMI, vol. 22. no. 9. (2000) 923–37
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Sung, J., Bang, SY. (2003). Hierarchical Bayesian Network for Handwritten Digit Recognition. In: Crowley, J.L., Piater, J.H., Vincze, M., Paletta, L. (eds) Computer Vision Systems. ICVS 2003. Lecture Notes in Computer Science, vol 2626. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36592-3_38
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DOI: https://doi.org/10.1007/3-540-36592-3_38
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