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A Neuro-Fuzzy based System for Classification of Natural Textures

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Abstract

A statistical approach based on the coordinated clusters representation of images is used for classification and recognition of textured images. In this paper, two issues are being addressed; one is the extraction of texture features from the fuzzy texture spectrum in the chromatic and achromatic domains from each colour component histogram of natural texture images and the second issue is the concept of a fusion of multiple classifiers. The implementation of an advanced neuro-fuzzy learning scheme has been also adopted in this paper. The results of classification tests show the high performance of the proposed method that may have industrial application for texture classification, when compared with other works.

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References

  1. M. Tuceryan, A.K. Jain, Texture analysis. in The Handbook of Pattern Recognition and Computer Vision, ed. by C.H. Chen, P. Wang (World Scientific Publishing, Singapore, 1993), Chapter 11, pp. 235–276

  2. P. Szczepaniak, P. Lisboa, J. Kacprzyk, Fuzzy Systems in Medicine (Springer, Berlin, 2000)

    Book  MATH  Google Scholar 

  3. M.G.A. Thomson, D.H. Foster, Role of second and third order statistics in the discriminability of natural images. J. Opt. Soc. Am. A: 14(9), 2081–2090 (1997)

    Article  Google Scholar 

  4. R.M. Haralick, K. Shanmugam, I. Dinstein, Textural features for image classification. IEEE Trans. Syst. Man Cybern. 3, 610–621 (1973)

    Article  Google Scholar 

  5. A. Goon, J.P. Rolland, Texture classification based on comparison of second order statistics I: 2P-PDF estimation and distance measure. J. Opt. Soc. Am. A: 16(7), 1566–1574 (1999)

    Article  Google Scholar 

  6. H. Dong-Chen, L. Wang, Texture unit, texture spectrum and texture analysis. IEEE Trans. Geo Sci. Remote Sens. 28(4), 509–512 (1990)

    Article  Google Scholar 

  7. J. Weszka, C. Dyer, A. Rosenfeld, A comparative study of texture measures for terrain classification. IEEE Trans. Syst. Man Cybern. SMC-6, 269–285 (1976)

    Article  MATH  Google Scholar 

  8. M. Unser, Local linear transforms for texture measurements. Signal Process. 11, 61–79 (1986)

    Article  MathSciNet  Google Scholar 

  9. T. Ojala, M. Pietik€ainen, D. Harwood, A comparative study of texture measures with classification based on feature distributions. Pattern Recogn. 29(1), 51–59 (1996)

    Article  Google Scholar 

  10. T. Ojala, K. Valkealahti, M. Pietikainen, Texture discrimination with multidimensional distributions of signed gray level differences. Pattern Recogn. 34, 21–33 (2000)

    MATH  Google Scholar 

  11. G.W. Jiji, L. Ganesan, A new approach for unsupervised segmentation. J. Soft Comput. 10(3), 689–693 (2010)

    Article  Google Scholar 

  12. R. Haralick, Statistical and structural approaches to texture. IEEE Proc. 67, 786–804 (1979)

    Article  Google Scholar 

  13. M. Boulougoura, E. Wadge, V.S. Kodogiannis, H.S. Chowdrey, Intelligent systems for computer-assisted clinical endoscopic image analysis. in Second IASTED International Conference on Biomedical Engineering, Innsbruck, Austria, 2004, ed. by Ulougoura et al., pp. 405–408

  14. G.W. Jiji, L. Ganesan, Comparative analysis of colour models for colour textures based on feature extraction. Int. J. Soft Comput. 2(3), 361–366 (2007)

    Google Scholar 

  15. L.I. Kuncheva, Fuzzy Classifier Design (Physica-Verlag, Wurzburg, 2002)

    MATH  Google Scholar 

  16. S. Mitra, S.K. Pal, P. Mitra, Data mining in soft computing framework: a survey. IEEE Trans. Neural Netw. 13(1), 3–14 (2002)

    Article  Google Scholar 

  17. S. Chen, C.H. Cowan, P. Grant, Orthogonal least-squares learning algorithm for radial basis function networks. IEEE Trans. Neural Netw. 2, 302–309 (1991)

    Article  Google Scholar 

  18. M. Orr, in Regularised Centre Recruitment in Radial Basis Function Networks. Research Report No. 59, Centre for Cognitive Science (University of Edinburgh, UK, 1993)

  19. S. Chen, E. Chng, Regularised orthogonal least squares algorithm for constructing radial basis function networks. Int. J. Control 64(5), 829–837 (1996)

    Article  MathSciNet  Google Scholar 

  20. B. Sujatha, V. VijayaKumar, M.C. Mohan, Rotationally invariant texture classification using LRTM based on fuzzy approach. Int. J. Comput. Appl. 33(4), 1–5 (2011)

    Google Scholar 

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Correspondence to G. Wiselin Jiji.

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Jiji, G.W. A Neuro-Fuzzy based System for Classification of Natural Textures. J. Inst. Eng. India Ser. B 97, 453–462 (2016). https://doi.org/10.1007/s40031-016-0224-x

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