Article Sidebar

Download
PDF
Statistic
Read Counter : 441 Download : 314

Downloads

Download data is not yet available.

Main Article Content

Abstract

 Texture is an important visual property that characterizes a wide range of natural and artificial images which makes it a useful feature for retrieving images. Several approaches have been proposed to describe the texture contents of an image. In early research works, such as edge histograms-based techniques and co-occurrence-based approaches, texture descriptors were mainly extracted from the spatial domain. Later on, dual spaces (transform of spatial domain) such as frequency space or spaces resulting from Gabor or wavelet transforms were explored for texture characterization. Recent physiological studies showed that human visual system can be modeled as a set of independent channels of various orientations and scales, this finding motivated the proliferation of multi-resolution methods for describing texture images. Most of these methods are either wavelet-based or Gabor-based. This paper summarizes our recent study of the use of Fourier-based techniques for characterizing image textures. At first, a singleresolution Fourier-based technique is proposed and its performance is compared against the performance of some classical Fourier-based methods. The proposed technique is then extended into a multi-resolution version. Performance of the modified technique is compared against those of the single-resolution approach and some other multi-resolution approaches recently described in literature. Two performance indicators were used in this comparison: retrieval accuracy and execution time of the techniques.

 

Keywords

Fourier Transform Texture-based image Retrieval Gabor filters Wavelet transform Multiresolution approach

Article Details

How to Cite
Abdesselam, A. (2010). A Multi-Resolution Texture Image Retrieval Using Fast Fourier Transform. The Journal of Engineering Research [TJER], 7(2), 48–58. https://doi.org/10.24200/tjer.vol7iss2pp48-58
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX

References

  1. Amadasun, M. and King, R., 1989, "Textural Features Corresponding to Textural Properties," IEEE SMC, Vol. 19, pp. 1264-1274.
  2. Beck et al. 1987, " Spatial Frequency Channels and Perceptual Grouping in Texture Segregation," Comp. Vision Graphics and Image Processing, Vol. 37, pp. 299-325.
  3. Bianconi, F. and Fernandez, A., 2007, " Evaluation of the Effects of Gabor Filter Parameters on Texture Classification,” Pattern Recognition, Vol. 40, pp. 3325-3335.
  4. Campbell, F.W. and Robson, J.G., 1968, " Application of Fourier Analysis to the Visibility of Gratings," Journal of Physiology, Vol. 197, pp. 551-556.
  5. Celik, T. and Tjahjadi, T., 2009, "Multiscale Texture Classification using Dual-tree Complex Wavelet Transform," Pattern Recognition Letters, Vol. 30, pp. 331-339.
  6. Chen, C.C. and Chen, C.C., 1999, "Filtering Methods for Texture Discrimination," Pattern Recognition Letters, Vol. 20, pp. 783-790.
  7. Conners, R.W. and Harlow, C.A., 1980, "A Theoretical Comparison of Texture Algorithms," IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI), vol. 2, pp. 204-222.
  8. Daugman, J.G. and Kammen, D.M., 1987, "Image Statistics. Gases, and Visual Neural Primitives," Proc. Of IEEE ICNN 4, pp. 163-175.
  9. Fountain, S.R. and Tan, T.N., 1998, "Efficient Rotation Invariant Texture Features for Contentbased Image Retrieval," Pattern Recognition, Vol. 31, pp. 1725-1732.
  10. Gibson, D. and Gaydecki, P.A., 1995, " Definition and Application of a Fourier Domain Texture Measure: Application to Histological Image Segmentation," Comp. Biol. Vol. 25, pp. 51-557
  11. Haralick, R.M., Shanmugam, K. and Dinstein, J., 1973, "Textural Features for Image Classification," IEEE Trans. Systems, Man and Cybernetics, Vol. 3, pp. 610-621.
  12. Huang, K. and Aviyente, S., 2008, "Wavelet Selection for Image Classification," IEEE Trans. On Image Processing, Vol. 17, pp. 1709-1720.
  13. Huang, P.W. and Dai, S.K. 2004, "Design of a Twostage Content-based Image Retrieval System using Texture Similarity," Information Processing and Management Vol. 40, pp. 81-96.
  14. Huang, P.W. and Dai, S.K., 2003, " Image Retrieval by Texture Similarity," Pattern Recognition, Vol. 36, pp. 665-679.
  15. Huang, P.W., Dai, S.K. and Lin, P.L., 2006, "Texture Image Retrieval and Image Segmentation using Composite Sub-band Gradient Vectors," J. Vis. Communication and Image Representation, Vol. 17, pp. 947-957.
  16. Jain, A.K. and Farrokhnia, F., 1991, " Unsupervised Texture Segmentation using Gabor Filters," Pattern Recognition, Vol. 24, pp. 1167-1186.
  17. Kankahalli, M., Mehtre, B.M. and Wu, J.K. 1996, "Cluster-based Color Matching for Image Retrieval," Pattern Recognition, Vol. 29, pp. 701- 708.
  18. Kokare, M., Biswas, P.K. and Chatterji, B.N., 2007, "Texture Image Retrieval using Rotated Wavelet Filters," Pattern Recognition Letters, Vol. 28, pp. 1240-1249.
  19. Kokare, M., Biswas, P.K. and Chatterji, B.N., 2005, " Texture Image Retrieval using New Rotated Complex Wavelet Filters," IEEE Trans. On Systems, Man, and Cybernetics B, Vol. 35, pp. 1168-1178.
  20. Kokare, M., Biswas, P.K. and Chatterji, B.N., 2006, " Rotation-invariant Texture Image Retrieval using Rotated Complex Wavelet Filters, IEEE Trans. On Systems, Man, and Cybernetics Vol. B 36, pp. 1273-1282.
  21. Kokare, M., Biswas, P.K. and Chatterji, B.N., 2005, " Texture Image Retrieval using New Rotated Complex Wavelet Filters," IEEE Trans. On Systems, Man, and Cybernetics, Vol. B 35, pp. 1168-1178.
  22. Selesnick, I.W., 2002, " The Design of Approximate Hilbert Transform Pairs of Wavelet Bases," IEEE Trans. Signal Processing, Vol. 50, pp. 1144-1152.
  23. Smith, J.R. and S-F., 1994, "Transform Features for Texture Classification and Discrimination in Large Image Databases," Image processing proceedings, ICIP-94, Vol. 3, pp. 407-411.