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A new approach for rotation-invariant and noise-resistant texture analysis and classification

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Abstract

The analysis and classification of images, such as texture images, is one of the substantial and important fields in image processing. Due to destructive effects of image rotation and noise, the stability and efficiency of texture analysis and classification methods are an important research area. In this paper, a new method for texture analysis and classification has been proposed which is based on a particular combination of wavelet, ridgelet and Fourier transforms as well as support vector machine. The proposed method has been evaluated for 13 texture datasets produced by three original datasets containing 25 and 111 original textures from Brodatz database and 24 original textures from OUTEX database. These datasets comprise 415584 and 93600 rotated noise-free and noisy texture images for Brodatz database and also 49920 noisy and 4320 noise-free texture images for OUTEX database, respectively. Simulation results demonstrate the capability, efficiency and also stability of the proposed method especially for real-time rotation-invariant and noise-resistant texture analysis and classification.

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References

  1. Arivazhagan, S., Ganesan, L., Subash Kumar, T.G.: Texture classification using ridgelet transform. Pattern Recognit. Lett. 27, 1875–1883 (2006)

    Article  Google Scholar 

  2. Avci, E., Sengur, A., Hanbay, D.: An optimum feature extraction method for texture classification. Expert Syst. Appl. 36, 6036–6043 (2009)

    Article  Google Scholar 

  3. Karahaliou, A.N., Boniatis, I.S.: Breast cancer diagnosis: analyzing texture of tissue surrounding microcalcifications. IEEE Trans. Inf. Technol. Biomed. 12(6), 731–738 (2008)

    Article  Google Scholar 

  4. Pun, C.M., Lee, M.C.: Log-polar wavelet energy signatures for rotation and scale invariant texture classification. IEEE Trans. Pattern Anal. Mach. Intell. 25, 590–603 (2003)

    Article  Google Scholar 

  5. Šuch, O., Barreda, S.: Bayes covariant multi-class classification. Pattern Recognit. Lett. 84, 99–106 (2016)

    Article  Google Scholar 

  6. Junior, J.J.D.M.S., Backes, A.R., Cortez, P.C.: Texture analysis and classification using shortest paths in graphs. Pattern Recognit. Lett. 34, 1314–1319 (2013)

    Article  Google Scholar 

  7. Fnliii, S., Liaig, N.: Classification of SAR image based on gray co-occurrence matrix and support vector machine. In: ICSP’O4 Proceedings, pp. 1385–1388 (2004)

  8. Cheng, G., Zhou, P., Han, J.: Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images. IEEE Trans. Geosci. Remote Sens. 54, 7405–7415 (2016)

    Article  Google Scholar 

  9. Tuceyran, M., Jain, A.K.: Texture analysis. In: Handbook of Pattern Recognition and Computer Vision, Chapter 2.1, 2nd edn, pp. 207–248. World Scientific Publishing Co. (1998)

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

    Article  Google Scholar 

  11. Conners, R.W., Harlow, C.A.: A theoretical comparison of texture algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 2, 204–222 (1980)

    Article  MATH  Google Scholar 

  12. Petrou, M., Sevilla, P.G.: Image Processing Dealing with Texture, pp. 329–379 and 436–520. Wiley, Hoboken (2006)

  13. Avci, E.: Comparison of wavelet families for texture classification by using wavelet packet entropy adaptive network based fuzzy inference system. Soft. Comput. 8, 225–231 (2008)

    Article  Google Scholar 

  14. Feraidooni, M.M., Ghofrani, S.: Texture analysis and classification dealing with rotation and different noises. World Appl. Sci. J. 14(2), 221–227 (2011)

    Google Scholar 

  15. Hadizadeh, H.: Multi-resolution local Gabor wavelets binay patterns for gray-scale texture description. Pattern Recognit. Lett. 65, 163–169 (2015)

    Article  Google Scholar 

  16. Lasmar, N.E., Baussard, A., Le Chenadec, G.: Asymmetric power distribution model of wavelet sub-bands for texture classification. Pattern Recognit. Lett. 52, 1–8 (2014)

    Article  Google Scholar 

  17. Dagher, I., Issa, S.: Subband effect of the wavelet fuzzy C-means features in texture classification. Image Vis. Comput. 30, 896–905 (2012)

    Article  Google Scholar 

  18. Jafari-Khouzani, K., Soltanian-Zadeh, H.: Radon transform orientation estimation for rotation invariant texture analysis. IEEE Trans. Pattern Anal. Mach. Intell. 27(6), 1004–1008 (2005)

    Article  Google Scholar 

  19. Qi, X., Shen, L., Zhao, G., Li, Q., Pietikäinen, M.: Globally rotation invariant multi-scale co-occurrence local binary pattern. Image Vis. Comput. 43, 16–26 (2015)

    Article  Google Scholar 

  20. Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24, 971–987 (2002)

    Article  MATH  Google Scholar 

  21. Guo, Z., Zhang, L., Zhang, D.: Rotation invariant texture classification using LBP variance (LBPV) with global matching. Pattern Recognit. 43, 706–719 (2010)

    Article  MATH  Google Scholar 

  22. Pan, W., Bui, T.D., Suen, C.Y.: Rotation invariant texture classification by ridgelet transform and frequency-orientation space decomposition. Sig. Process. 88, 189–199 (2008)

    Article  MATH  Google Scholar 

  23. Qiao, Y.L., Song, C.Y., Zhao, C.H.: M-band ridgelet transform based texture classification. Pattern Recognit. Lett. 31, 1875–1883 (2010)

    Google Scholar 

  24. Dharmagunawardhana, C., Mahmoodi, S., Bennett, M., Niranjan, M.: Rotation invariant texture descriptors based on Gaussian Markov random fields for classification. Pattern Recognit. Lett. 69, 15–21 (2016)

    Article  Google Scholar 

  25. Ahmadvand, A., Daliri, M.R.: Invariant texture classification using a spatial filter bank in multi-resolution analysis. Image Vis. Comput. 45, 1–10 (2016)

    Article  Google Scholar 

  26. Shakoor, M.H., Boostani, R.: Extended mapping local binary pattern operator for texture classification. Int. J. Pattern Recognit. Artif. Intell. 31(6), 1750019, 1–22 (2017)

  27. Feraidooni, M.M., Ghofrani, S.: Texture analysis and classification using a rotation and noise invariant method. In: The 3rd Int. Conference on Machine Vision, pp. 246–249 (2010)

  28. Candes, E.J.: Ridgelets: theory and applications. Ph.D. Thesis, Technical Report, Department of Statistics, Stanford University (1998)

  29. Candes, E.J., Donoho, D.L.: Ridgelets: a key to higher-dimensional intermittency? Philos. Trans. R. Soc. Lond. Ser. A. 357, 2495–2509 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  30. Do, M.N., Vetterli, M.: The finite ridgelet transform for image representation. IEEE Trans. Image Process. 12, 16–28 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  31. Chan, A.K., Peng, C.: Wavelets for Sensing Technologies, pp. 21–22. Artech House Inc., Norwood (2003)

  32. https://www.csie.ntu.edu.tw/~jlin/libsvm

  33. Brodatz, P.: Texture: A Photographic Album for Artists and Designers. Dover, New York (1966)

    Google Scholar 

  34. Outex Texture Database, Texture classification test suites, Center for Machine Vision Research, University of Oulu. http://www.outex.oulu.fi/index.php?page=classification (2007)

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Authors and Affiliations

  1. Department of Telecommunications, Faculty of Electrical Engineering, Shahid Beheshti University, G. C., Tehran, Iran

    Mohammad Mahdi Feraidooni & Davood Gharavian

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  1. Mohammad Mahdi Feraidooni
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  2. Davood Gharavian
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Correspondence to Davood Gharavian.

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Feraidooni, M.M., Gharavian, D. A new approach for rotation-invariant and noise-resistant texture analysis and classification. Machine Vision and Applications 29, 455–466 (2018). https://doi.org/10.1007/s00138-017-0899-2

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  • DOI: https://doi.org/10.1007/s00138-017-0899-2

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