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Texture Feature-Based Image Classification Using Wavelet Package Transform

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Advances in Intelligent Computing (ICIC 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3644))

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Abstract

In this paper, a new method based on wavelet package transform is proposed for classification of texture images. It has been demonstrated that a large amount of texture information of texture images is located in middle-high frequency parts of image, a corresponding method called wavelet package transform, not only decomposing image from the low frequency parts, but also from the middle-high frequency parts, is presented to segment texture images into a few texture domains used for image classification. Some experimental results are obtained to indicate that our method for image classification is superior to the co-occurrence matrix technique obviously.

This work was supported by the National Science Foundation of China (Nos.60472111 and 60405002).

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References

  1. Livens, S.: Image Analysis for Material Characterization. PhD Thesis. University of Antwerp. Antwerp. Belgium (1998)

    Google Scholar 

  2. Randen, T.: Filter and Filter Bank Design for Image Texture Recognition. PhD Thesis. NTNU. Stavanger. Norway (1997)

    Google Scholar 

  3. Van de Wouwer, G.: Wavelets for Multiscale Texture Analysis. PhD Thesis. University of Antwerp. Antwerp. Belgium (1998)

    Google Scholar 

  4. Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Addison-Wesley, Reading (1992)

    Google Scholar 

  5. Haralick, R.M.: Statistical and Structural Approaches to Texture. Proc. IEEE 67, 768–804 (1979)

    Article  Google Scholar 

  6. Pitas, I.: Digital Image Processing Algorithms and Applications. Wiley, New York (2000)

    Google Scholar 

  7. Ahuja, N., Rosenfeld, A.: Mosaic Models for Textures. IEEE Trans. Anal. Mach. Intell. 3(1), 1–11

    Google Scholar 

  8. Huang, D.S.: Systematic Theory of Neural Networks for Pattern Recognition. Publishing House of Electronic Industry of China, Beijing (1996)

    Google Scholar 

  9. Cohen, F.S., Fan, Z., Patel, M.A.: Classification of Rotation and Scaled Textured Images Using Gaussian Markov Random Field Models. IEEE Trans. Pattern Anal. 13(2), 192–202 (1991)

    Article  Google Scholar 

  10. Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural Features for Image Classification. IEEE Trans. Systems Man Cybernet. SMC-3, 610–621 (1973)

    Google Scholar 

  11. Haralick, R.M., Shapiro, L.S.: Computer Vision, vol. 1. Addison Wesley, Reading (1992)

    Google Scholar 

  12. Ohanian, P.P., Dubes, R.C.: Performance Evaluation for Four Classes of Textural Features. Pattern Recognition 25(8), 819–833 (1992)

    Article  Google Scholar 

  13. Kashyap, R.L., Chellappa, R.: Estimation and Choice of Neighbors in Spatial-Interaction Models of Images. IEEE Trans. Inf. Theory TT-29(1), 60–72 (1983)

    Google Scholar 

  14. Kashyap, R.L., Khotanzao, A.: A Model-Based Method for Rotation Invariant Texture Classification. IEEE Trans. Pattern Analysis Mach. Intell. PAMI-8(4), 472–481 (1986)

    Google Scholar 

  15. Unser, M.: Texture Classification and Segmentation Using Wavelet Frames. IEEE Trans. Image Process. 4(11), 1549–1560 (1995)

    Article  Google Scholar 

  16. Coifman, R.R., Meyer, Y., Wickerhauser, M.V.: Progress in Wavelet Analysis and Applications. Editions Frontieres, France (1993)

    Google Scholar 

  17. Wu, W.-R., Wei, S.-C.: Rotation and Gray Scale Transform Invariant Texture Classification Using Spiral Resampling, Subband Decomposition and Hidden Markov Model. IEEE Trans. Image Process. 5(10), 1423–1433 (1996)

    Article  Google Scholar 

  18. Pontil, M., Verri, A.: Supporl Vector Machines for 3D Object Recognition. IEEE Trans. Parrem Analysis and Machine Intelligence 20(6), 637–646 (1998)

    Article  Google Scholar 

  19. Huang, D.S., Ma, S.D.: Linear and Nonlinear FeedForward Neural Network Classifiers: A Comprehensive Understanding. Journal of Intelligent Systems 9(1), 1–38 (1999)

    MathSciNet  Google Scholar 

  20. Huang, D.S.: Radial Basis Probabilistic Neural Networks: Model and Application. International Journal of Pattern Recognition and Artificial Intelligence 13(7), 1083–1101 (1999)

    Article  Google Scholar 

  21. Ma, Y.J.: Texture Image Classification Based on Support Vector Machine and Distance Classification. In: Proceeding of 4th World Congress on Intelligence and Automation

    Google Scholar 

  22. Hwang, W.J., Wen, K.W.: Fast kNN Classification Algorithm Based on Partial Distance Search. Electron. Lett. 34(21), 2062–2063 (1998)

    Article  Google Scholar 

  23. Mallat, S.: A Theory for Multiresolution Signal Decomposition: the Wavelet Representation. IEEE Trans. Patt. Anal. Machine Intell. 11, 674–693 (1989)

    Article  MATH  Google Scholar 

  24. Chang, T., Kuo, C.-C.J.: Texture Analysis and Classification with Tree-Structured Wavelet Transform. IEEE Transactions on Image Processing 2(4), 429–441 (1993)

    Article  Google Scholar 

  25. Soman, A.K., Vaidyanathan, P.P.: On Orthonormal Wavelets and Paraunitary Filter Banks. IEEE Trans. Signal Processing 41(3), 1170–1182 (1993)

    Article  MATH  Google Scholar 

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Authors and Affiliations

  1. Institute of Intelligent Machines, Chinese Academy of Sciences, P.O.Box 1130, Hefei, Anhui, 230031, China

    Yue Zhang, Xing-Jian He & Jun-Hua Han

Authors
  1. Yue Zhang
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  2. Xing-Jian He
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  3. Jun-Hua Han
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Editor information

Editors and Affiliations

  1. Intelligent Computing Lab, Institute of Intelligent Machines, Chinese Academy of Sciences,, China

    De-Shuang Huang

  2. School of Computer & Information Technology, Beijing Jiaotong University, 100044, Beijing, P.R. China

    Xiao-Ping Zhang

  3. School of Electrical and Electronic Engineering, Nanyang Technological University, P.O. Box, Singapore

    Guang-Bin Huang

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© 2005 Springer-Verlag Berlin Heidelberg

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Zhang, Y., He, XJ., Han, JH. (2005). Texture Feature-Based Image Classification Using Wavelet Package Transform. In: Huang, DS., Zhang, XP., Huang, GB. (eds) Advances in Intelligent Computing. ICIC 2005. Lecture Notes in Computer Science, vol 3644. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11538059_18

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  • DOI: https://doi.org/10.1007/11538059_18

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28226-6

  • Online ISBN: 978-3-540-31902-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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