International Journal of Image, Graphics and Signal Processing(IJIGSP)

ISSN: 2074-9074 (Print), ISSN: 2074-9082 (Online)

Published By: MECS Press

IJIGSP Vol.4, No.12, Nov. 2012

A New Approach for Texture Classification Based on Average Fuzzy Left Right Texture Unit Approach

Full Text (PDF, 400KB), PP.57-64

Views:83   Downloads:1


Y Venkateswarlu, B Sujatha, J V R Murthy

Index Terms

Texture image, Texton pattern, Classification


Texture refers to the variation of gray level tones in a local neighbourhood. The “local” texture information for a given pixel and its neighbourhood is characterized by the corresponding texture unit. Based on the concept of texture unit, this paper describes a new statistical approach to texture analysis, based on average of the both fuzzy left and right texture unit matrix. In this method the “local” texture information for a given pixel and its neighbourhood is characterized by the corresponding fuzzy texture unit. The proposed Average Fuzzy Left and Right Texture Unit (AFLRTU) matrices overcome the disadvantage of FTU by reducing the texture unit from 2020 to 79. The proposed scheme also overcomes the disadvantage of the left and right texture unit matrix (LRTM) by considering the texture unit numbers from all the 4 different LRTM’s instead of the minimum one as in the case of LRTM. The co-occurrence features extracted from the AFLRTU matrix provide complete texture information about an image, which is useful for texture classification. Classification performance is compared with the various fuzzy based texture classification methods. The results demonstrate that superior performance is achieved by the proposed method.

Cite This Paper

Y Venkateswarlu,B Sujatha,J V R Murthy,"A New Approach for Texture Classification Based on Average Fuzzy Left Right Texture Unit Approach", IJIGSP, vol.4, no.12, pp.57-64, 2012.


[1]Kaplan, L.M., Extended fractal analysis for texture classification and segmentation, IEEE Transactions on Image Processing, Vol. 8, No. 11, pp.1572-1585, 1999.

[2]R.M.Haralick, “Statistical and structural approaches to texture," Proc.IEEE, vol. 67, no. 5, pp. 786-804, 1979

[3]L.Kirvida, “Texture measurements for the automatic classification of imagery," IEEE Trans. Electromagnet. Compat, vol. 18, no. 2, pp. 38-42, 1976.

[4]A.Rosenfeld and M.Thurston, “Edge and curve detection for visual scene analysis," IEEE Trans. Comput, vol.20, no.5, pp. 562-569, 1971

[5]R.M.Haralick, K.Shanmugam, and I.Dinstein, “Texture features for image classi_cation," IEEE Trans.Syst.Man Cybernet, vol. 3, no. 11, pp. 610-621, 1973.

[6]S.Z.Li, “Markov random field modeling in computer vision," Springer-Verlag Tokyo, 1995

[7]A.Kundu and J.L.Chen, “Texture classification using qmf bank based sub band decomposition," CVGIP: Graphical models and image processing, vol. 54, no. 5, pp. 369-384, 1992.

[8]Wu, W.-R., Wei, S.-C.: Rotation and Gray-Scale Transform-Invariant Texture Classification Using Spiral Resampling, Subband Decomposition and Hidden Markov Model. IEEE Trans. Image Processing 5 (1996) 1423-1434.

[9]Chen, J.-L., Kundu, A.: Rotation and Gray Scale Transform Invariant Texture Identification Using Wavelet Decomposition and Hidden Markov Model. IEEE Trans. Pattern Analysis and Machine Intelligence 16 (1994) 208-214.

[10]D.C.He, L.Wang and J.Guibert, "Texture features extraction," Pattern Recognition Letters. Vol.6, pp.269- 273, 1987

[11]Dong-Chen He and Li Wang, “Texture unit, texture spectrum and Texture, IEEE Transactions on geoscience and Remote Sensing, Vol. 28, July 1990

[12]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

[13]L.Wang, D.C.He, “A new statistical approach to texture analysis,” Photogrammetrics Eng. and Remote Sensing, pp.61-65, 1990

[14]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

[15]L.Wang and D.C.He, "Texture classification using texture spectrum," Pattern Recognition, 1989

[16]J.S. Taur, C.W. Tao, Texture classification using a fuzzy texture spectrum and neural networks, J. Electron. Imaging 7 (1) (1998) 29–35

[17]A.Barcelo, E.Montseny and P.Sobrevilla. “On Fuzzy Texture Spectrum for Natural Microtextures Characterization,” Proceedings EUSFLAT-LFA, pp. 685-690, 2005

[18]Sujatha.B, Vijayakumar.V, Chandra Mohan M., “ Rotationally Invariant Texture Classification using LRTM based on Fuzzy Approach,” IJCA, vol.33, November 2011

[19]G. Wiselin Jiji a,, L. Ganesan, “A new approach for unsupervised segmentation,” Applied Soft Computing 10 (2010) 689–693.

[20]M.Ramabai, V.Venkata Krishna, J.Sasi Kiran, “Morphological Shape features for Classification of Textures based on Fuzzy Texture Element,” IJCA, Volume 19– No.7, April 2011

[21]Abdulrahman A. AL-JANOBI and AmarNishad M. THOTTAM, “Testing and Evaluation of Cross-Diagonal Texture Matrix Method.

[22]Abdulrahman Al-Janobi, “Performance evaluation of cross-diagonal texture matrix method of texture analysis,” Pattern Recognition, vol.34, pp: 171-180, 2001