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Noise robust and rotation invariant texture classification based on local distribution transform

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Abstract

Applying local binary pattern (LBP) to images with uniform distribution leads to generate discriminative features; however, the distribution of all images is not necessarily uniform. The distribution of an image can be uniformzed if it passes through its cumulative distribution function (CDF), while estimation of CDF is highly sensitive to additive noises. In this paper, we propose a novel transform, which locally uniformize all patches of an image and approximately estimate a robust CDF. The proposed local distribution transform (LDT) generates continuous values and by quantizing them into discrete values, a histogram of features is constructed. We have fused the LDT features to the features of rotation invariant LBP and local variance (VAR) in order to provide a rich set of robust-to-noise features, which can detect both uniform and non-uniform patterns. The performance of the proposed LDT-LBP_VAR is assessed over a wide range of datasets like Outex, UIUC, CUReT, Coral Reef, Virus and ORL. The datasets are also corrupted by additive Gaussian noise with different signal to noise ratio (SNR) and the empirical results demonstrate that the proposed hybrid features provide superior classification results (P < 0.05) to the plenty of advanced descriptors over the datasets in both noise-free and noisy conditions.

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References

  1. Ahonen T, Pietikainenn M (2007) in Proceedings of the Finnish Signal Processing Symposium, FINSIG, vol. 1. Soft histograms for local binary patterns, pp. 1-4, Oulu, Finland, 2007

  2. Ahonen T, Hadid A, Pietikäinen M (2006) Face recognition with local binary patterns: application to face recognition. IEEE Trans. on Pattern Analysis and Machine Intelligence 28(12):2037–2041

    Article  Google Scholar 

  3. Anys H, He DC (1995) Evaluation of textural and multi polarization radar features for crop classification. IEEE Trans Geosci Remote Sens 33(5):1170–1181

    Article  Google Scholar 

  4. AT&T Laboratories (2002) Database of faces. http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html. Accessed 1 May 2019

  5. Bruna J, Mallat S (2013) Invariant scattering convolution networks. IEEE Trans Pattern Analysis and Machine Intelligence 35(8):1872–1886

    Article  Google Scholar 

  6. Chan T, Jia K, Gao S, Lu J, Zeng Z, Ma Y (2015) PCANet: a simple deep learning baseline for image classification? IEEE TransImage Processing 24(12):5017–5032

    Article  MathSciNet  Google Scholar 

  7. Cimpoi M, Maji S, Vedaldi A (June 2015) Deep filter banks for texture recognition and segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition, (CVPR), pp. 3828–3836

  8. Cimpoi M, Maji S, Kokkinos I, Vedaldi A (2016) Deep filter banks for texture recognition, description, and segmentation. Int J Comput Vis 118:65–94. https://doi.org/10.1007/s11263-015-0872-3

    Article  MathSciNet  Google Scholar 

  9. Cohen FS, Fan Z, Attali S (1991) Automated inspection of textile fabrics using textural models. IEEE Trans Pattern Anal Mach Intell 13(8):803–808

    Article  Google Scholar 

  10. Dana KJ, van Ginneken B, Nayar SK, Koenderink JJ (1999) Reflectance and texture of real world surfaces. ACM Trans Graph 18(1):1–34

    Article  Google Scholar 

  11. Deng H, Clausi DA (2004) Gaussian VZ-MRF rotation-invariant features for image classification. IEEE Trans. on Pattern Analysis and Machine Intelligence 26(7):951–955

    Article  Google Scholar 

  12. Fathi A, Naghsh-Nilchi A (2012) Noise tolerant local binary pattern operator for efficient texture analysis. Pattern Recognition Letters

  13. Galloway M (1975) Texture analysis using gray level run lengths. Computer Graphics and Image Processing 4(2):172–199

    Article  Google Scholar 

  14. Guo Z, Zhang L, Zhang D (2014) A completed modeling of local binary pattern Pperator for texture classification. IEEE Trans Image Process 9(16):1657–1663

    MATH  Google Scholar 

  15. Robert M. Haralick, K Shanmugam Its’HakDinstein (1979) Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics

  16. Huang X, Li SZ, Wang Y (2004) Shape localization based on statistical method using extended local binary patterns, in Proc. International Conference on Image and Graphics, pp.184–187

  17. Ji Q, Engel J, Craine E (2000) Texture analysis for classification of cervix lesions. IEEE Trans Med Imaging 19(11):1144–1149

    Article  Google Scholar 

  18. Kashyap RL, Khotanzed A (1986) A model-based method for rotation invariant texture classification. IEEE Trans on Pattern Analysis and Machine Intelligence 8(4):472–481

    Article  Google Scholar 

  19. Kylberg G (2012) Virus texture dataset v. 1.0, http://www.cb.uu.se/~gustaf/virustexture. Accessed 20 May 2019

  20. Lazebnik S, Schmid C, Ponce J (2005) A sparse texture representation using local affine regions. IEEE Trans. on Pattern Analysis and Machine Intelligence 27(8):1265–1278

    Article  Google Scholar 

  21. Liao S, Law M, Chung A (2009) Dominant local binary patterns for texture classification. IEEE Trans Image Process 18(5):1107–1118

    Article  MathSciNet  Google Scholar 

  22. Liu L, Long Y, Fieguth P, Lao S, Zhao G (2014) BRINT: binary rotation invariant and noise tolerant texture classification. IEEE Trans. on Image Processing 23(7):3071–3084

    Article  MathSciNet  Google Scholar 

  23. Liu L, Fieguth P, Wang X, Pietikäinen MI, Hu D (2016). Evaluation of LBP and deep texture descriptors with a new robustness Benchmark. 9907. 69–86. https://doi.org/10.1007/978-3-319-46487-9_5.

  24. Mir AH, Hanmandlu M, Tandon SN (1995) Texture analysis of CT images, in: Engineering in Medicine and Biology Magazine, vol.14, IEEE

  25. Murala S, Maheshwari RP, subramanian RB (2012) Local tetra patterns: a new feature descriptor for content-based image retrieval. IEEE Transactions on Image Processing 21(5):2874–2886

    Article  MathSciNet  Google Scholar 

  26. Ojala T (1997) Nonparametric texture analysis using simple spatial operators, with applications in visual inspection. Acta University at is Oulu ensis, C 105

  27. Ojala T, Pietikäinen M, Harwood D (1996) A comparative study of texture measures with classification based on feature distributions. Pattern Recognition 29(1):51–59

    Article  Google Scholar 

  28. Ojala T, Mäenpää T, Pietikäinen M, Viertola J, Kyllönen J, Huovinen S (2002) Outex – new framework for empirical evaluation of texture analysis algorithm, in Proc. International Conference on Pattern Recognition, pp. 701–706

  29. Ojala T, Pietikainen M, Maenpa TT (2002) Multiresolution gray-scale and rotation Invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7):971–987

  30. Papoulis A, Pillai SU (2002) Probability, random variables, and stochastic processes. McGraw-Hill, Boston

    Google Scholar 

  31. Pietikäinen M, Ojala T, Xu Z (2000) Rotation-invariant texture classification using feature distributions. Pattern Recogn 33(1):43–52

    Article  Google Scholar 

  32. Qiao Y, Zhao Y (2015) Rotation invariant texture classification using principal direction estimation and random projection. Journal of Information Hiding and Multimedia Signal Processing. 6. pp. 534–543

  33. Qiao Y-L, Lu Z-M, Pan J-S, Sun S-H (2009) Fast k-nearest neighbor search algorithm based on pyramid structure of wavelet transform and its application to texture classification. Digital Signal Processing 20:837–845. https://doi.org/10.1016/j.dsp.2009.10.011

    Article  Google Scholar 

  34. Ren J, Jiang X, Yuan J (2013) Noise resistant local binary pattern with an embedded error correction mechanism. IEEE Trans Image Process 22(10):4049–4060

    Article  MathSciNet  Google Scholar 

  35. Shakoor MH, Boostani R (2017) A novel advanced local binary pattern for image-based coral reef classification. Multimedia Tools and Applications, pp. 1–31. https://doi.org/10.1007/s11042-017-4394-6

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

    Article  Google Scholar 

  37. Shakoor MH, Boostani R (2018) Radial mean local binary pattern for noisy texture classification. Multimedia Tools and Applications pp. 1–28

  38. Shakoor MH, Tajeripour F (2016) Noise robust and rotation invariant entropy features for texture classification. Multimed Tools Appl 75(6):1–36

    Google Scholar 

  39. Shakoor MH, Tajeripour F (2017) Repeating average filter for noisy texture classification, Scientia Iranica. Transaction D, Computer Science & Engineering, Electrical 24(3):1419–1436

    Google Scholar 

  40. Song T, Li H, Meng F, Wu Q, Luo B, Zeng B, Gabbouj M (2014) Noise-robust texture description using local contrast patterns via global measures. IEEE Signal Process Letter 21(1):93–96

    Article  Google Scholar 

  41. Sotoodeh M, Moosavi MR, Boostani R (2019) A novel adaptive LBP-based descriptor for color image retrieval. Expert Systems with Applications 127:42–352

    Article  Google Scholar 

  42. Talab ARR, Shakoor MH (2018) Fabric classification using new mapping of local binary pattern. International Conference on Intelligent Systems and Computer Vision, pp. 1–4

  43. Tan X, Triggs B (2007) Enhanced local texture feature sets for face recognition under difficult lighting conditions. in Proc. International Workshop on Analysis and Modeling of Faces and Gestures, pp.168–182

  44. Varma M, Garg R (2007) Locally invariant fractal features for statistical texture classification, in Proc. International Conference on Computer Vision, pp.1–8

  45. Varma M, Zisserman A (2005) A statistical approach to texture classification from single images. Int J Comput Vis 62(1–2):61–81

    Article  Google Scholar 

  46. Varma M, Zisserrman A (2009) A statistical approach to material classification using image patch exemplars. IEEE Trans. on Pattern Analysis and Machine Intelligence 31(11):2032–2047

    Article  Google Scholar 

  47. Wang JW, Chen, CH, Pan JS (1998) Genetic feature selection for texture classification using 2-d non-separable wavelet bases. IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences. E81A. pp. 1635–1644

  48. Yuan F, Xia X, Shi J (2018) Mixed co-occurrence of local binary patterns and hamming-distance-based local binary patterns, Information Sciences pp 202–222

  49. Zhang J, Marszalek M, Lazebnik S, Schmid C (2007) Local features and kernels for classification of texture and object categories: a comprehensive study. Int J Comput Vis 73(2):213–238

    Article  Google Scholar 

  50. Zhang B, Gao Y, Zhao S, Liu J (2010) Local derivative patterns versus local binary patterns: face recognition with high-order local patterns descriptor. IEEE Trans Image Process 19(2):533–544

    Article  MathSciNet  Google Scholar 

  51. Zhao Y, Jia W, Hu RX, Min H (2013) Completed robust local binary pattern for texture classification. Neurocomputing 106:6876

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Engineering, Faculty of Engineering, Arak University, Arak, 38156-8-8349, Iran

    Mohammad Hossein Shakoor

  2. Computer Engineering Department. School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran

    Reza Boostani

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  1. Mohammad Hossein Shakoor
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  2. Reza Boostani
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Correspondence to Mohammad Hossein Shakoor.

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Shakoor, M.H., Boostani, R. Noise robust and rotation invariant texture classification based on local distribution transform. Multimed Tools Appl 80, 8639–8666 (2021). https://doi.org/10.1007/s11042-020-10084-4

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