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Improving Texture Recognition using Combined GLCM and Wavelet Features

by Ranjan Parekh
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 29 - Number 10
Year of Publication: 2011
Authors: Ranjan Parekh
10.5120/3597-4991

Ranjan Parekh . Improving Texture Recognition using Combined GLCM and Wavelet Features. International Journal of Computer Applications. 29, 10 ( September 2011), 41-46. DOI=10.5120/3597-4991

@article{ 10.5120/3597-4991,
author = { Ranjan Parekh },
title = { Improving Texture Recognition using Combined GLCM and Wavelet Features },
journal = { International Journal of Computer Applications },
issue_date = { September 2011 },
volume = { 29 },
number = { 10 },
month = { September },
year = { 2011 },
issn = { 0975-8887 },
pages = { 41-46 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume29/number10/3597-4991/ },
doi = { 10.5120/3597-4991 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:15:28.361921+05:30
%A Ranjan Parekh
%T Improving Texture Recognition using Combined GLCM and Wavelet Features
%J International Journal of Computer Applications
%@ 0975-8887
%V 29
%N 10
%P 41-46
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Texture is an important perceptual property of images based on which image content can be characterized and searched for in a Content Based Search and Retrieval (CBSR) system. This paper investigates techniques for improving texture recognition accuracy by using a set of Wavelet Decomposition Matrices (WDM) in conjunction with Grey Level Co-occurrence Matrices (GLCM). The texture image is decomposed at 3 levels using a 2D Haar Wavelet and a coefficient computed from the decomposition matrices is combined with features derived from a set of normalized symmetrical GLCMs computed along four directions, to provide improved accuracy. The proposed scheme is tested on a set of 13 textures derived from the Brodatz database and is seen to provide accuracies of the order of 90%.

References
  1. Laws, K. I. 1980. Textured image segmentation. Doctoral Thesis. University of Southern California, Los Angeles, CA, USCIPI Rep. 940.
  2. Conners, R. W., Trivedi, M. M., and Harlow, C. A. 1984. Segmentation of a High Resolution Urban Scene using Texture Operators. Computer Vision, Graphics and Image Processing, vol. 25, 273-310.
  3. Dinstein, I., Fong, A. C., Ni, L. M., and Wong, K. Y. 1984. Fast Discrimination between Homogeneous and Textured Regions. In Proceedings of the 7th International Conference on Pattern Recognition, Montreal, Canada, 361-363.
  4. Wang, R., Hanson, A. R. and Riseman, E. M. 1986. Texture Analysis based on Local Standard Deviation of Intensity. In Proceedings of the Conference on Computer Vision and Pattern Recognition, Florida, USA, 481-488.
  5. Tamura, H., Mori, S., and Yamawaki, T. 1978. Textural Features corresponding to Visual Perceptions. IEEE Transactions on Systems, Man and Cybernetics, vol. 8, no. 6, 460-473.
  6. Pentland, A. P. 1984. Fractal based Description of Natural Scenes. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 6, no. 6, 661-674.
  7. Khotanzad, A., and Bouarfa, A. 1988. A Parallel Non-parametric Non-iterative Clustering Algorithm with Application to Image Segmentation. In Proceedings of the 22nd Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, 305-309.
  8. Huang, N. K. 1984. Markov Model for Image Segmentation. In Proceedings of the 22nd Allerton Conference on Communication, Control and Computing, Montecello, 775-781.
  9. Bovik, A. C., Clark, M., and Geisler, W. S. 1990. Multichannel Texture Analysis using Localized Spatial Filters. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, no. 1, 55-73.
  10. Manjunath, B. S., and Chellappa, R. 1993. A Unified Approach to Boundary Perception : Edges, Textures and Illusory Contours. IEEE Transactions on Neural Networks, vol. 4, no. 1, 96-108.
  11. Deguchi, K., and Morishita, I. 1978. Texture Characterization and Texture-based Image Partitioning using Two-dimensional Linear Estimation Techniques. IEEE Transactions on Computers, vol. C-27, no. 8, 739-745.
  12. Haralick, R. M. 1979. Statistical and Structural Approaches to Texture. Proceedings of IEEE, vol. 67, 786–804.
  13. Graps, A. 1995. An Introduction to Wavelets. IEEE Computational Science and Engineering, vol. 2, no. 2, 50-61.
  14. Brodatz, P. 1966. Textures : A photographic album for artists and designers. Dover Publications, NY. (http://www.ux.uis.no/~tranden/brodatz.html).
Index Terms

Computer Science
Information Sciences

Keywords

Texture recognition Grey Level Co-occurrence Matrix Wavelet decomposition Content Based Storage and Retrieval Pattern Recognition

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