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Rotationally Invariant Texture Classification using LRTM based on Fuzzy Approach

International Journal of Computer Applications
© 2011 by IJCA Journal
Volume 33 - Number 4
Year of Publication: 2011
B. Sujatha
Dr. V. VijayaKumar
M. Chandra Mohan

B Sujatha, Dr. V VijayaKumar and Chandra M Mohan. Article: Rotationally Invariant Texture Classification using LRTM based on Fuzzy Approach. International Journal of Computer Applications 33(4):1-5, November 2011. Full text available. BibTeX

	author = {B. Sujatha and Dr. V. VijayaKumar and M. Chandra Mohan},
	title = {Article: Rotationally Invariant Texture Classification using LRTM based on Fuzzy Approach},
	journal = {International Journal of Computer Applications},
	year = {2011},
	volume = {33},
	number = {4},
	pages = {1-5},
	month = {November},
	note = {Full text available}


Texture is an important spatial feature, useful for identifying objects or regions of interest in an image. One of the most popular statistical methods used to measure the textural information of images is the grey-level co-occurrence matrix (GLCM). The other statistical approach to texture analysis is the texture spectrum approach. The present paper combines the fuzzy texture unit and GLCM approach to derive a Left Right Texture Unit Matrix (LRTM). The LRTM approach considers the two sets of four connected texture elements on a 3×3 grid for evaluating the TU instead of non-connected or corner texture elements as in the case of Cross Diagonal Texture Unit Matrix (CDTM). The co-occurrence features extracted from the LRTM provide complete texture information about an image, which is useful for classification. The performance of these features for classification/discrimination of the texture images has been evaluated. The LRTM texture features are compared with original texture spectrum features in discriminating/classification of some of the VisTex natural texture images. The proposed LRTM reduces the size of the matrix from 6561 to 79 as in the case of original texture spectrum and 2020 to 79 as in the case of fuzzy texture spectrum approach. Thus it reduces the overall complexity. The experimental results indicate the efficacy of the proposed method.


  • R.M. Haralick, "Statistical and structural approaches to texture," Proc. of IEEE, Vol.67, pp.786-804, 1979.
  • C. S. Lu, P.C. Chung and C.F. Chen, “Unsupervised texture segmentation via wavelet transform,” Pattern Recognition, Vol. 30, no. 5, pp. 729-742, 1997.
  • O. Pichler, A. Teuner and B. J. Hosticka, “A comparison of texture extraction using adaptive Gabor filtering, pyramidal and tree structured wavelet transforms,” Pattern Recognition, Vol. 29, no. 5, pp.733-742,1996.
  • B. Julesz, “Visual pattern discrimination," IRE Trans. Inform. Theory, Vol.8, pp.84-92, 1962.
  • A. Gagalowicz, "Visual discrimination of stochastic texture fields based upon second order statistics," Proc. 5Th Int. Joint Conf. Pattern Recog., Miami Beach, pp.786- 789, 1980.
  • R.M. Haralick, K. Shanmugan and I. Dinstein, "Textural features for image classification," IEEE Trans. Sysr., Man., Cybern., Vol. SMC-3, pp.610-621, 1973.
  • R.W. Conners, "Towards a set of statistical features which measure visually perceivable qualities of texture," Proc. Pattern Recognition Image Processing Conf. pp.382-390, 1979.
  • D.C.He, L.Wang and J.Guibert, "Texture features extraction," Pattern Recognition Letters. Vol.6, pp.269- 273, 1987.
  • Dong-Chen He and Li Wang, “Texture unit, texture spectrum and Texture, IEEE Transactions on geoscience and Remote Sensing, Vol. 28, July 1990.
  • D.C.He, L.Wang, “Texture features based on texture spectrum,” Pattern Recognition, Vol.24, pp. 391-399, 1991.
  • D.C.He and L.Wang, "Texture unit, texture spectrum and texture analysis," Proc of IGARSS.89, Vancouver, Canada, 1989, Vo1.5, pp.2769-2772.
  • L.Wang, D.C.He, “A new statistical approach to texture analysis,” Photogrammetrics Eng. and Remote Sensing, pp.61-65, 1990.
  • L. Wang, D.C. He and A. Fabbri, "Textural filtering for SAR image processing," Proc. of IGARSS'89, Vancouver, Canada, 1989, Vol.5, pp. 2785-2788, 1989.
  • L.Wang and D.C.He, "Texture classification using texture spectrum," Pattern Recognition, 1989.
  • A.Barcelo, E.Montseny and P.Sobrevilla. “On Fuzzy Texture Spectrum for Natural Microtextures Characterization,” Proceedings EUSFLAT-LFA, pp. 685-690, 2005.
  • T. Ojala, M. Pietiknen, D. Harwood, “A comparative study of texture measures with classification based on feature distributions,” Pattern Recognition, Vol.29, pp.51-59, 1996
  • Abdulrahman Al-Janobi, “Performance evaluation of cross-diagonal texture matrix method of texture analysis,” Pattern Recognition, Vol. 34, pp.171-180, 2001.
  • G.Wiselin Jiji, L.Ganesan, “A new approach for unsupervised segmentation,” Applied Soft Computing, Vol.10, pp.689-693, 2010.
  • VisTex.ColourImageDatabase http://
  • An introduction to texture tiling using characteristics of the texture itself. Available at