Journal of KIISE:Software and Applications (한국정보과학회논문지:소프트웨어및응용)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
권호 표지

Hierarchical Gabor Feature and Bayesian Network for Handwritten Digit Recognition

계층적인 가버 특징들과 베이지안 망을 이용한 필기체 숫자인식

  • 성재모 (포항공과대학교 컴퓨터공학과) ;
  • 방승양 (포항공과대학교 컴퓨터공학과)
  • Published : 2004.01.01
Download PDF
⟨ Previous Next ⟩

Abstract

For the handwritten digit recognition, this paper Proposes a hierarchical Gator features extraction method and a Bayesian network for them. Proposed Gator features are able to represent hierarchically different level information and Bayesian network is constructed to represent hierarchically structured dependencies among these Gator features. In order to extract such features, we define Gabor filters level by level and choose optimal Gabor filters by using Fisher's Linear Discriminant measure. Hierarchical Gator features are extracted by optimal Gabor filters and represent more localized information in the lower level. Proposed methods were successfully applied to handwritten digit recognition with well-known naive Bayesian classifier, k-nearest neighbor classifier. and backpropagation neural network and showed good performance.

본 논문에서는 필기체 숫자인식을 위해서 계층적으로 서로 다른 레벨의 정보를 표현할 수 있는 구조화된 특징들의 추출 방법과 특징들 사이에 의존도를 이용하여 분류하는 베이지안 망을 제안한다. 이러한 계층적 특징들을 추출하기 위해서 레벨 단위로 가버 필터들을 정의하고, FLD(Fisher Linear Discriminant) 척도를 이용하여 최적화된 가버 필터들을 선택한다. 계층적 가버 특징들은 최적화된 가버 특징들을 이용하여 추출되며, 하위 레벨일수록 더욱 국부적인 정보를 표현한다. 추출된 계층적 가버 특징들의 분류성능 향상을 위해서 가버 특징들 사이의 계층적 의존도를 이용하는 베이지안 망을 생성한다. 본 논문에서 제안하는 방법은 naive Bayesian 분류기, k-nearest neighbor 분류기, 그리고 신경망 분류기들과 함께 필기체 숫자인식에 적용되어 계층적 가버 특징들의 효율성과 계층적 의존도를 이용하는 베이지안 망은 분류성능을 향상시킬 수 있다는 것을 보여준다.

Keywords

References

  1. Gabor, D. : Theory of communication, J. Inst. Electr. Engng., vol. 93, pp.. 429-459, 1946
  2. Bishop, C. M. : Neural Networks for Pattern Recognition. Oxford University Press, 1995
  3. Hoyer, P. O. and Hyarinen, A. : Independent component analysis applied to feature extraction from colour and stereo images. Network : Computation in Neural Systems, vol, 11, no. 3, pp. 191-210, 2000 https://doi.org/10.1088/0954-898X/11/3/302
  4. Porat, M. and Zeevi, Y. Y. : The generalized Gabor scheme of image representation in biological and machine vision. IEEE trans. PAMI, vol. 10, pp. 452-468, 1988 https://doi.org/10.1109/34.3910
  5. Daugman, J. G. : Two-dimensional spectral analysis of cortical receptive field profiles. Vision Res, vol. 20, pp. 847-856, 1980 https://doi.org/10.1016/0042-6989(80)90065-6
  6. Hamamoto, Y., Uchimura, S., Watanabe, M., Yasuda, T., Mitani, Y., and Tomita, S. : A Gabor Filter-Based Method for Recognizing Handwritten Numerals. Pattern Recognition, vol. 31, no. 4, pp. 395-400, 1998 https://doi.org/10.1016/S0031-3203(97)00057-5
  7. Daijin Kim and Sung-Yang Bang : A Handwritten Numeral Character Classification Using Tolerant Rough Set. IEEE trans. PAMI, vol. 22, no. 9, pp. 923-937, 2000 https://doi.org/10.1109/34.877516
  8. Tai Sing Lee. : Image Representation Using 2D Gabor Wavelets. IEEE trans. PAMI, vol. 18, no. 10, pp. 959-971, 1996 https://doi.org/10.1109/34.541406
  9. Pearl, J. : Probabilistic Inference in Intelligent Systems. Morgan Kaufmann, San Mateo,California, 1988
  10. Heckerman, D. and Geiger, D. : A tutorial on learning with Bayesian networks. In M.I.Jordan (Ed.), Learning in graphical models, Kluwer, 1998
  11. Cowell, R. G., Dawid, A. P., Lauritzen, S. L., and Spiegelhater, D. J. : Probabilistic Networks and Expert Systems. Springer, 1999
  12. Lauritzen, S. L. and Spiegelhalter, D. J. : Local computations with probabilities on graphical structures and their applications to expert systems. J. Royal Statist. Soc. B, vol. 50, no. 2, pp. 154-227, 1988
  13. Friedman, N., Geiger, D. and Goldszmidt, M. : Bayesian network classifiers. Mach. Learn, vol. 29, pp. 131-163, 1997 https://doi.org/10.1023/A:1007465528199
  14. Blake, C. I. and Merz, C. J. : UCI Repository of Machine Learning Databases. Dept. of Information and Computer Science, University of California, Irvine, 1998