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Texture Analysis Using Multidimensional Histogram

Published on March 2012 by Payel Saha, Sudhir Sawarkar
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International Conference and Workshop on Emerging Trends in Technology
Foundation of Computer Science USA
ICWET2012 - Number 8
March 2012
Authors: Payel Saha, Sudhir Sawarkar
5d4bfdc1-e576-453a-8287-fc3d15ad9073

Payel Saha, Sudhir Sawarkar . Texture Analysis Using Multidimensional Histogram. International Conference and Workshop on Emerging Trends in Technology. ICWET2012, 8 (March 2012), 13-17.

@article{
author = { Payel Saha, Sudhir Sawarkar },
title = { Texture Analysis Using Multidimensional Histogram },
journal = { International Conference and Workshop on Emerging Trends in Technology },
issue_date = { March 2012 },
volume = { ICWET2012 },
number = { 8 },
month = { March },
year = { 2012 },
issn = 0975-8887,
pages = { 13-17 },
numpages = 5,
url = { /proceedings/icwet2012/number8/5368-1059/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference and Workshop on Emerging Trends in Technology
%A Payel Saha
%A Sudhir Sawarkar
%T Texture Analysis Using Multidimensional Histogram
%J International Conference and Workshop on Emerging Trends in Technology
%@ 0975-8887
%V ICWET2012
%N 8
%P 13-17
%D 2012
%I International Journal of Computer Applications
Abstract

Texture features have long been used in remote sensing applications for representing and retrieving regions similar to a query region. Various representations of texture have been proposed based on the power spectrum, grey-level co-occurrence matrices, wavelet features, Gabor features, etc. Analysis of several co-occurring pixel values may benefit texture description but is impeded by the exponential growth of histogram size. Multidimensional histograms can be reduced by using methods like linear compression, dimension optimization and vector quantization. Experiments with natural textures showed that multidimensional histograms provided higher classification accuracies than the channel histograms and the wavelet packet signatures

References
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  41. R.M. Haralick, “Statistical and Structural Approaches to Texture,” Proc. IEEE, vol. 67, no. 5, pp. 786–804, May 1979.
  42. D.-C. He and L. Wang, “Unsupervised Textural Classification of Images Using the Texture Spectrum,” Pattern Recognition, vol. 25, no. 3, pp. 247–255, 1992.
  43. V. Kovalev and M. Petrou, “Multidimensional Co-Occurrence Matrices for Object Recognition and Matching,” Graphical Models and Image Processing, vol. 58, no. 3, pp. 187–197, May 1996.
  44. E. Oja and K. Valkealahti, “Co-Occurrence Map: Quantizing Multidimensional Texture Histograms,” Pattern Recognition Letters, vol. 17, no. 7, pp. 723–730, June 1996.
  45. M. Unser, “Local Linear Transforms for Texture Measurements,” Signal Processing, vol. 11, no. 1, pp. 61–79, July 1986.
  46. A. Laine and J. Fan, “Texture Classification by Wavelet Packet Signatures,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 15, no. 11, pp. 1,186–1,191, Nov. 1993.
  47. G.F. McLean, “Vector Quantization for Texture Classification,” IEEE Trans. Systems, Man, and Cybernetics, vol. 23, no. 3, pp. 637– 649, May/June 1993.
  48. T. Kohonen, Self-Organizing Maps. Berlin: Springer-Verlag, 1995.
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  50. R.M. Gray, “Vector Quantization,” IEEE ASSP Magazine, vol. 1, no. 2, pp. 4–29, Apr. 1984.
  51. A. Gersho and R.M. Gray, Vector Quantization and Signal Compression. Boston: Kluwer Academic Publishers, 1992.
  52. P. Brodatz, Textures: A Photographic Album for Artists and Designers. New York: Dover Publications, 1966.
  53. L. Davis, ed., Handbook of Genetic Algorithms. New York: Van Nostrand Reinhold, 1991.
  54. M. Vetterli and J. Kovacevic, Wavelets and Subband Coding. Englewood Cliffs, N.J.: Prentice Hall, 1995.
  55. D.E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning. Reading, Mass.: Addison-Wesley Publishing Company, 1989.
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Index Terms

Computer Science
Information Sciences

Keywords

Texture classification multidimensional histograms vector quantization self-organizing map feature selection