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International Conference on Artificial Neural Networks

ICANN 2007: Artificial Neural Networks – ICANN 2007 pp 546–553Cite as

Global and Local Preserving Feature Extraction for Image Categorization

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4669))

Abstract

In this paper, we describe a feature extraction method: Global and Local Preserving Projection (GLPP). GLPP is based on PCA and the recently proposed Locality Preserving Projection (LPP) method. LPP can preserve local information, while GLPP can preserve both global and local information. In this paper we investigate the potential of using GLPP for image categorization. More specifically, we experiment on palmprint images. Palmprint image has been attracting more and more attentions in the image categorization/recognition area in recent years. Experiment is based on benchmark dataset PolyU, using Error Rate as performance measure. Comparison with LPP and traditional algorithms show that GLPP is promising.

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References

  1. Feng, G.Y., Hu, D.W., Li, M., Zhou, Z.T.: Palmprint Recognition Based on Unsupervised Subspace Analysis. In: Wang, L., Chen, K., Ong, Y.S. (eds.) ICNC 2005. LNCS, vol. 3610, pp. 675–678. Springer, Heidelberg (2005)

    Google Scholar 

  2. Connie, T., Jin, A.T.B, B., K., Ong, M.G., Ling, D.N.C.: An Automated Palmprint Recognition System. Image and Vision Computing 23, 501–515 (2005)

    Article  Google Scholar 

  3. Wu, X.Q., Zhang, D., Wang, K.Q.: Fisherpalms Based Palmprint Recognition. Pattern Recognition Letters 24, 2829–2838 (2003)

    Article  Google Scholar 

  4. Lu, G.M., Zhang, D., Wang, K.Q.: Palmprint Recognition Using Eigenpalms. Pattern Recognition Letters 24, 1463–1467 (2003)

    Article  MATH  Google Scholar 

  5. Kumara, A., Zhang, D.: Personal Authentication Using Multiple Palmprint Representation. Pattern Recognition 38, 1695–1704 (2005)

    Article  Google Scholar 

  6. Kong, W.K., Zhang, D., LiW, X.: Palmprint Feature Extraction using 2-D Gabor Filters. Pattern Recognition 36, 2339–2347 (2003)

    Article  Google Scholar 

  7. Yang, J., Zhang, D., Yang, J.-Y.: Is ICA Significantly Better than PCA for Face Recognition? ICCV, 198–203 (2005)

    Google Scholar 

  8. Wu, X., Wang, K., Zhang, D.: Palmprint Authentication Based on Orientation Code Matching. In: Kanade, T., Jain, A., Ratha, N.K. (eds.) AVBPA 2005. LNCS, vol. 3546, pp. 555–562. Springer, Heidelberg (2005)

    Google Scholar 

  9. Bazaraa, M.S., Sherali, H.D., Shetty, C.M.: Nonlinear programming: theory and algorithms. John Wiley &Sons Inc., New York (1993)

    MATH  Google Scholar 

  10. Belkin, M., Niyogi, P.: Laplacian eigenmaps and spectral techniques for embedding and clustering. In: Dietterich, T.G., Becker, S., Ghahramani, Z. (eds.) Advances in Neural Information Processing Systems, vol. 14, MIT Press, Cambridge, MA (2002)

    Google Scholar 

  11. He, X., Niyogi, P.: Locality preserving projection. In: Advances in Neural Information Processing Systems 16 (NIPS 2003), Vancouver, Canada, MIT Press, Cambridge (2003)

    Google Scholar 

  12. He, X., Cai, D., Min, W.: Statistical and computational analysis of locality preserving projection. In: International Conference on Machine Learning (ICML), Bonn, Germany (2005)

    Google Scholar 

  13. Kocsor, A., Csirik, J.: Fast Independent Component Analysis in Kernel Feature Spaces. In: Pacholski, L., Ružička, P. (eds.) SOFSEM 2001. LNCS, vol. 2234, pp. 271–281. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  14. Liu, Q.S., Cheng, J., et al.: Modeling Face Appearance with Kernel Independent Component Analysis. In: Sixth IEEE International Conference on Automatic Face and Gesture Recognition (FGR2004), May 17-19, Seoul, Korea, pp. 761-766 (2004)

    Google Scholar 

  15. Francis, R.B., Jordan, M.I.: Kernel independent component analysis. Journal of Machine Learning Research 3, 1–48 (2002)

    Google Scholar 

  16. Aha, D., Kibler, D.: Instance-based Learning Algorithms. Machine Learning 6, 37–66 (1991)

    Google Scholar 

  17. The PolyU Palmprint Database: http://www.comp.polyu.edu.hk/biometrics/

  18. Zeng, X.-Y., Chen, Y.-W., Nakao, Z., Lu, H.: A New Texture Feature based on PCA Maps and Its Application to Image Retrieval. IEICE Trans. Inf. and Syst. E86-D(5), 929–936 (2003)

    Google Scholar 

  19. Diamantaras, K.I., Kung, S.Y.: Principal Component Neural Networks: Theory and Applications. John Wiley & Sons, INC, Chichester (1996)

    MATH  Google Scholar 

  20. Amari, S.: Natural Gradient for Over- and Under-complete Bases in ICA. Neural Computation 11(8), 1875–1883 (1999)

    Article  Google Scholar 

  21. Cardoso, J.F., Laheld, B.H.: Equivariant Adaptive Source Separation. IEEE Trans. Signal Processing 44(12), 3017–3030 (1996)

    Article  Google Scholar 

  22. He, X., Niyogi, P.: Locality preserving projections. Technical Report TR-2002-09, University of Chicago Computer Science (October 2002)

    Google Scholar 

  23. Hu, Y.Q.: Locality preserving projection for post and expression classification (2006), www.ntu.edu.sg/home/aswduch/Teaching/Assign1-vis/HuYiqun.pdf

  24. Ali, G.N., Chiang, P.-J., Mikkilineni, A.K., Chiu, G.T.-C., Allebach, J.P., Delp, E.J.: Application of principal components analysis and Gaussian mixture models to printer identification. In: Proceedings of the IS&T’s NIP20: International Conference on Digital Printing Technologies, pp. 301–305 (2004)

    Google Scholar 

  25. Addison D., Wermter S., Arevian G.: A Comparison of Feature Extraction and Selection Techniques. In: Proceedings of the International Conference on Artificial Neural Networks, Istanbul, Turkey, pp. 212–215 (June 2003)

    Google Scholar 

  26. Jenkins, O.C.: Relative localization from pairwise distance relationships using kernel pca. Technical Report CRES-03-010, Center for Robotics and Embedded Systems, University of Southern California (April 2003)

    Google Scholar 

  27. Choi, S., Cichocki, A., Amari, S.: Flexible independent component analysis. Journal of VLSI Signal processing 26, 25–38 (2000)

    Article  MATH  Google Scholar 

  28. Mallat, S.: A theory for multiresolution signal decomposition: the wavelet representation. IEEE Pattern Anal. and Machine Intell. 11(7), 674–693 (1989)

    Article  MATH  Google Scholar 

  29. Daubechies, I.: Ten lectures on wavelets, CBMS-NSF conference series in applied mathematics. SIAM Ed. (1992)

    Google Scholar 

  30. Pang, Y.-H., Jin, A.T.B., Ling, D.N.C.: Palmprint Authentication System Using Wavelet based Pseudo Zernike Moments Features. International Journal of The Computer, the Internet and Management 13(2), 13–26 (2005)

    Google Scholar 

  31. Gan, J., Liang, Y.: Applications of Wavelet Packets Decomposition in Iris Recognition. ICB 2006, 443–449 (2006)

    Google Scholar 

  32. Scholkopf, B., Smola, A., Muller, K.-R.: Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation 10, 1299–1319 (1998)

    Article  Google Scholar 

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Authors and Affiliations

  1. College of Information Science and Technology, Beijing Normal University, Beijing 100875, P.R. China

    Rongfang Bie, Xin Jin, Chuan Xu, Chuanliang Chen, Anbang Xu & Xian Shen

Authors
  1. Rongfang Bie
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  2. Xin Jin
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  3. Chuan Xu
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  4. Chuanliang Chen
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  5. Anbang Xu
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  6. Xian Shen
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Editor information

Joaquim Marques de Sá Luís A. Alexandre Włodzisław Duch Danilo Mandic

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© 2007 Springer-Verlag Berlin Heidelberg

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Bie, R., Jin, X., Xu, C., Chen, C., Xu, A., Shen, X. (2007). Global and Local Preserving Feature Extraction for Image Categorization. In: de Sá, J.M., Alexandre, L.A., Duch, W., Mandic, D. (eds) Artificial Neural Networks – ICANN 2007. ICANN 2007. Lecture Notes in Computer Science, vol 4669. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74695-9_56

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  • DOI: https://doi.org/10.1007/978-3-540-74695-9_56

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-74693-5

  • Online ISBN: 978-3-540-74695-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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