Submit Manuscript

Information Technology Journal

Year: 2008 | Volume: 7 | Issue: 6 | Page No.: 883-889
Research Article

Texture Classification Based on Extraction of Skeleton Primitives Using Wavelets

U.S.N. Raju
Department of CSE and IT, Godavari Institute of Engineering and Technology, Rajahmundry, Andra Pradash, India
B. Eswara Reddy
Department of CSE, JNTU College of Engineering, Anantapur, Andra Pradash, India
V. Vijaya Kumar
Department of CSE and IT, Godavari Institute of Engineering and Technology, Rajahmundry, Andra Pradash, India
B. Sujatha
Department of CSE and IT, Godavari Institute of Engineering and Technology, Rajahmundry, Andra Pradash, India
A novel method for dominant skeleton extraction of textures using different wavelet transforms, is proposed in this study. The skeleton varies depending on the shape of structuring element. If the structuring element is homothetic to the object, the object is covered with only one magnification of the structuring element. By this, the skeleton is reduced to one point. The present study considers the skeleton from a binary texture. The proposed method derives from the above that a total number of pixels within the skeleton is the minimum when structuring element is homothetic to the primitive. This provides the scope that the texture is composed of one primitive, which minimizes the total number of pixels. For evaluating such skeleton primitive the present study utilized a 3x3 structuring element, as the skeleton primitives. All possible skeleton primitives combinations of 3x3 mask are evaluated on all textures. The skeleton primitive that is making the least number of skeleton points is considered as dominant skeleton primitive. Based on the extraction of skeleton primitives a classification is made on textures using Haar, Daubechies, Coiflet and Symlet wavelets. Experimental results indicate a good classification and also a comparison is made among these four wavelet results. Present method is experimented on Brodatz textures using these four wavelets.
PDF Fulltext XML References Citation

Leave a Comment

Your email address will not be published. Required fields are marked *

Article Trend

Total views 3321

References

  1. Antonini, M., M. Barlaud, P. Mathieu and I. Daubechies, 1992. Image coding using wavelet transform. IEEE Trans. Image Process., 1: 205-220.
    CrossRefDirect Link
  2. Blum, H., 1967. A Transformation for Extracting New Descriptors of Shape: Models for the Perception of Speech and Visual Form. 1st Edn., MIT Press, Cambridge, pp: 362-380.
  3. Bovik, A.C., M. Clark and W.S. Geisler, 1990. Multichannel texture analysis using localized spatial filters. IEEE Trans. Pattern Anal. Machine Intel., 12: 55-73.
    CrossRef
  4. Brodatz, P., 1966. Textures: A Photographic Album for Artists and Designers. 1st Edn., Dover, New York.
  5. Chang, H.S. and H. Yan, 1999. Analysis of stroke structures of handwritten chinese characters. IEEE Trans. Syst., Man. Cybernetics B, 29: 47-61.
    Direct Link
  6. Chang, T. and C.C.J. Kuo, 1993. Texture analysis and classification with tree-structured wavelet transform. IEEE Trans. Image Process, 2: 429-441.
    CrossRef
  7. Chellappa, R. and S. Chatterjee, 1985. Classification of textures using gaussian markov random fields. IEEE Trans. Acoustics Speech Signal Process, 33: 959-963.
    CrossRef
  8. Chen, P.C. and T. Pavlidis, 1983. Segmentation by texture using correlation. IEEE Trans. Pattern Anal. Machine Intel., 5: 64-69.
    CrossRef
  9. Cohen, F.S., Fan, Z. and M.A. Patel, 1991. Classification of rotated and scaled textured images using gaussian markov random field models. IEEE Trans. Pattern Anal. Machine Intel., 13: 192-202.
    CrossRef
  10. Eswara Reddy, B., A. Nagaraja Rao, A. Suresh and V. Vijaya Kumar, 2007. Texture classification by simple patterns on edge direction movements. Int. J. Comput. Sci. Network Security, 7: 221-225.
    Direct Link
  11. Ge, Y. and J.M. Fitzpatrick, 1996. On the generation of skeletons from discrete euclidean distance maps, IEEE Trans. Pattern Anal. Machine Intel., 18: 1055-1066.
    CrossRef
  12. Haralick, R.M., K. Shanmugam and I.H. Dinstein, 1973. Textural features for image classification. IEEE Trans. Syst. Man Cybern., SMC-3: 610-621.
    CrossRefDirect Link
  13. Lam, L., S.W. Lee and C.Y. Suen, 1992. Thinning methodologies: A comprehensive survey. IEEE Trans. Pattern Anal. Machine Intel., 14: 869-885.
    CrossRef
  14. Ogniewicz, R.L. and O. Kubler, 1995. Hierarchic voronoi skeletons. Pattern Recognition, 28: 343-359.
    CrossRef
  15. Pavlidis, T., 1986. A vectorizer and feature extractor for document recognition. Computer vision, graphics. Image Proc., 35: 111-127.
    CrossRef
  16. Smith, R.W., 1987. Computer processing of line images: A survey. Pattern Recognition, 20: 7-15.
    CrossRef
  17. Suresh, A., U.S.N. Raju, A. Nagaraja Rao and V. Vijaya Kumar, 2008. An innovative technique of marble texture description based on grain components. Int. J. Comput. Sci. Network Security, 8: 122-126.
    Direct Link
  18. Unser, M., 1986. Local linear transforms for texture measurements. Signal Process, 11: 61-79.
    CrossRef
  19. Unser, M., 1995. Texture classification and segmentation using wavelet frames. IEEE Trans. Image Process, 4: 1549-1560.
    Direct Link
  20. Vijaya, K.V., B.E. Reddy, U.S.N. Raju and A. Suresh, 2008. Classification of textures by avoiding complex patterns. Sci. Publ., J. Comput., 4: 133-138.
    CrossRefDirect Link
  21. Vijaya, K.V., A Srikrishna, D.V.L.N. Somayajulu and B.R. Babu, 2008. An improved iterative morphological decomposition approach for image skeletonization. J. Graphics Vision Image Proc. ICGST, 8: 47-54.
    Direct Link
  22. Vijaya, K.V., A. Srikrishna, S.A. Shaik and S. Trinath, 2008. A new skeletonization method based on connected component approach. Int. J. Comput. Sci. Network Security, 8: 133-137.
    Direct Link
  23. Vijaya, K.V., B.E. Reddy, U.S.N. Raju and K.C. Sekharan, 2007. An innovative technique of texture classification and comparison based on long linear patterns. J. Comput. Sci., 3: 633-638.
    CrossRefDirect Link
  24. Vijaya, K.V., B.E. Reddy and U.S.N. Raju, 2007. A measure of patterns trends on various types of preprocessed images. Int. J. Comput. Sci. Network Security, 7: 253-257.
    Direct Link
  25. Vijaya, K.V., U.S.N. Raju, K.C. Sekaran and V.V. Krishna, 2008. A new method of texture classification using various wavelet transforms based on primitive patterns, ICGST. Int. J. Graphics Vision Image Proc., 8: 21-27.
  26. Weszka, J.S., C.R. Dyer and A. Rosenfeld, 1976. A comparative study of texture measures for terrain classification. IEEE Trans. Syst. Man. Cybernet., 6: 269-285.
    CrossRef
  27. Zou, J.J. and H. Yan, 1999. Extracting strokes from static line images based on selective searching. Pattern Recognition, 32: 935-946.
    Direct Link
  28. Daubechies, I., 1992. Ten lectures on wavelets. Rutgers University and AT and T Laboratories.
  29. Krishna, V.V., V. Vijaya Kumar, U.S.N. Raju and B. Saritha, 2005. Classification of textures based on distance function of linear patterns using mathematical morphology. Proceedings of ICEM, Conducted by JNT University, India.
  30. Laws, K.L., 1980. Rapid texture identification. Proceedings of the Seminar, Image Processing for Missile Guidance, July 29-August 1, 1980, San Diego, CA., pp: 376-380.
    Direct Link
  31. Raju, U.S.N., V. Vijaya Kumar, A. Suresh and M.R. Mani, 2008. Texture description using different wavelet transforms based on statistical parameters Proceedings of the 2nd WSEAS International Symposium on Wavelets Theory and Applications in Applied Mathematics, Signal Processing and Modern Science, May 27-30, 2008, Istanbul, Turkey, pp: 174-178.

Keywords