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The fusion feature wavelet pyramid based on FCIS and GLCM for texture classification

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Abstract

Local binary pattern (LBP) is an effective texture feature extraction algorithm, but sensitive to noise. This paper proposes an improved LBP algorithm named fusion center interval strategy (FCIS) for this problem. FCIS introduces the fuzzy method and presents an adaptive fusion center interval selection based on the local statistical information. To improve the completeness of feature extraction, a systematic texture feature extraction algorithm named the fusion feature wavelet pyramid (FFWP) has been proposed. FFWP is built on a set of flexible selectable filters of non-separable bidimensional Shannon type wavelets and fuses FCIS and gray-level co-occurrence matrix (GLCM). The FCIS and GLCM fusion features provide information on contrast, homogeneity, and local anisotropy. The wavelet pyramid with multi-level decomposition makes for multi-scale FCIS and GLCM fusion features with no need for the fixed radii. Validation experiments are performed on publicly available datasets, Kylberg, UMD, and UIUC datasets. Compared with 18 relative algorithms, the results show the effectiveness of FCIS with less time consumption and the supremacy of FFWP on robust and comprehensive texture representation.

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References

  1. Gudadhe S, Thakare A, Oliva D (2023) Classification of intracranial hemorrhage ct images based on texture analysis using ensemble-based machine learning algorithms: A comparative study. Biomed Signal Process Control 84:104832. https://doi.org/10.1016/j.bspc.2023.104832

    Article  Google Scholar 

  2. Hammad A, Shawkat A (2022) Applying modified LBP for 2D ECG images classification. In: Proceedings of the 2nd International Conference on Emerging Technologies and Intelligent Systems (ICETIS) 573, 22–31. https://doi.org/10.1007/978-3-031-20429-6_3

  3. Kamireddy R, Dhuli R (2023) Detection of brain tumors from MR images using fuzzy thresholding and texture feature descriptor. J Supercomput 79:1–32. https://doi.org/10.1007/s11227-022-05033-x

    Article  Google Scholar 

  4. Taneja K, Arora V, Verma K (2023) Classifying the heart sound signals using textural-based features for an efficient decision support system. Expert Syst 40(6). https://doi.org/10.1111/exsy.13246

  5. Liu Q, Song Y, Tang Q, Bu X, Hanajima N (2023) Wire rope defect identification based on ISCM-LBP and GLCM features. Vis Comput, 1–13. https://doi.org/10.1007/s00371-023-02800-6

  6. Liu H, Jia X, Su C, Yang H, Li C (2023) Tire appearance defect detection method via combining HOG and LBP features. Front Phys 10:1099261. https://doi.org/10.3389/fphy.2022.1099261

    Article  Google Scholar 

  7. Yuan H, Lei Z, You X, Dong Z, Zhang H, Zhang C, Zhao Y, Liu J (2023) Fault diagnosis of driving gear in rack and pinion drives based on multi-scale local binary pattern extraction and sparse representation. Meas Sci Technol 34(5). https://doi.org/10.1088/1361-6501/acbab4

  8. Karanwal S, Diwakar M (2023) Triangle and orthogonal local binary pattern for face recognition. Multimed Tools Appl, 1–27. https://doi.org/10.1007/s11042-023-15072-y

  9. Mathew D, Kumar DCS, Cherian KA (2023) Integration of nondecimated quaternion wavelet transform and neighborhood texture patterns for disease classification in banana (musa spp.) foliage. Multimed Tools Appl, 1–23. https://doi.org/10.1007/s11042-023-14869-1

  10. Bakheet S, Alsubai S, El-Nagar A, Alqahtani A (2023) A multi-feature fusion framework for automatic skin cancer diagnostics. Diagnostics 13(8):1474. https://doi.org/10.3390/diagnostics13081474

    Article  Google Scholar 

  11. Sut S, Koc M, Zorlu G, Serhatlioglu I, Barua PD, Dogan S, Baygin M, Tuncer T, Tan RS, Acharya UR (2023) Automated adrenal gland disease classes using patch-based center symmetric local binary pattern technique with CT images. J Digit Imaging 36:1–14. https://doi.org/10.1007/s10278-022-00759-9

    Article  Google Scholar 

  12. Chen H, Ma M, Liu G, Wang Y, Jin Z, Liu C (2023) Breast tumor classification in ultrasound images by fusion of deep convolutional neural network and shallow LBP feature. J Digit Imaging 36:1–15. https://doi.org/10.1007/s10278-022-00711-x

    Article  Google Scholar 

  13. Haralick R, Shanmugam K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst Man Cybern 3(6):610–621

    Article  Google Scholar 

  14. Zhu Y, Zhao L, Chen X, Li Y, Wang J (2023) Identification of cashmere and wool based on LBP and GLCM texture feature selection. J Eng Fibers Fabr 18. https://doi.org/10.1177/15589250221146548

  15. Fernández A, Álvarez Cid M, Bianconi F (2013) Texture description through histograms of equivalent patterns. J Math Imaging Vis 45(1):76–102. https://doi.org/10.1007/s10851-012-0349-8

    Article  MathSciNet  Google Scholar 

  16. Ojala T, Pietikainen M, Harwood D (1996) A comparative study of texture measures with classification based on featured distributions. Pattern Recognit 29(6):51–59. https://doi.org/10.1016/0031-3203(95)00067-4

    Article  Google Scholar 

  17. Li B, Li Y, Wu QMJ (2023) A completed parted region local neighborhood energy pattern for texture classification. Dig Signal Process 137:104031. https://doi.org/10.1016/j.dsp.2023.104031

    Article  Google Scholar 

  18. Lan S, Fan H, Hu S, Ren X, Liao X, Pan Z (2023) An edge-located uniform pattern recovery mechanism using statistical feature-based optimal center pixel selection strategy for local binary pattern. Expert Syst Appl 221:119763. https://doi.org/10.1016/j.eswa.2023.119763

    Article  Google Scholar 

  19. Akbal E, Barua PD, Dogan S, Tuncer T, Acharya UR (2023) Explainable automated anuran sound classification using improved one-dimensional local binary pattern and tunable Q wavelet transform techniques. Expert Syst Appl 225:120089. https://doi.org/10.1016/j.eswa.2023.120089

    Article  Google Scholar 

  20. Tan X, Triggs B (2010) Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans Image Process 23(6):1635–1650

    MathSciNet  Google Scholar 

  21. Song K-C, Yan Y, Zhao Y, Liu C (2015) Adjacent evaluation of local binary pattern for texture classification. J Vis Commun Image Represent 33:323–339. https://doi.org/10.1016/j.jvcir.2015.09.016

    Article  Google Scholar 

  22. Iakovidis D, Keramidas E, Maroulis D (2008) Fuzzy local binary patterns for ultrasound texture characterization. Int Conf Image Anal Recognit 5112(6):750–759. https://doi.org/10.1007/978-3-540-69812-8_74

    Article  Google Scholar 

  23. Liu L, Long Y, Fieguth P, Lao S-Y, Zhao G (2014) BRINT: binary rotation invariant and noise tolerant texture classification. IEEE Trans Image Process 23(7):3071–3084. https://doi.org/10.1109/TIP.2014.2325777

    Article  MathSciNet  Google Scholar 

  24. Zhao Y, Jia W, Hu R-X, Hai M (2013) Completed robust local binary pattern for texture classification. Neurocomputing 106:68–76. https://doi.org/10.1016/j.neucom.2012.10.017

    Article  Google Scholar 

  25. Luo Y, Sa J, Song Y, Jiang H, Zhang C, Zhang Z (2023) Texture classification combining improved local binary pattern and threshold segmentation. Multimed Tools Appl 82:1–18. https://doi.org/10.1007/s11042-023-14749-8

    Article  Google Scholar 

  26. Wang K, Bichot C-E, Li Y, Li B (2017) Local binary circumferential and radial derivative pattern for texture classification. Pattern Recognit 67:213–229. https://doi.org/10.1016/j.patcog.2017.01.034

    Article  Google Scholar 

  27. Guo Z, Zhang L, Zhang D (2010) A completed modeling of local binary pattern operator for texture classification. IEEE Trans Image Process 19(6):1657–1663. https://doi.org/10.1109/TIP.2010.2044957

    Article  MathSciNet  Google Scholar 

  28. Zhao Y, Huang D-S, Jia W (2012) Completed local binary count for rotation invariant texture classification. IEEE Trans Image Process 21(10):4492–4497. https://doi.org/10.1109/TIP.2012.2204271

    Article  MathSciNet  Google Scholar 

  29. Arora N, Sharma S (2023) The practical applications of HLBP texture descriptor. Multimed Tools Appl. https://doi.org/10.1007/s11042-023-14406-0

    Article  Google Scholar 

  30. Qian X, Hua X-S, Chen P, Ke L (2011) PLBP: an effective local binary patterns texture descriptor with pyramid representation. Pattern Recognit 44:2502–2515. https://doi.org/10.1016/j.patcog.2011.03.029

    Article  Google Scholar 

  31. Wu X, Sun J (2017) Joint-scale LBP: a new feature descriptor for texture classification. Vis Comput 33(3):317–329. https://doi.org/10.1007/s00371-015-1202-z

    Article  Google Scholar 

  32. Koley S, Roy H, Dhar S, Bhattacharjee D (2023) Cross-modal face recognition with illumination-invariant local discrete cosine transform binary pattern (LDCTBP). Pattern Anal Appl 26:1–13. https://doi.org/10.1007/s10044-023-01139-x

    Article  Google Scholar 

  33. Pan Z, Li Z, Fan H, Wu X (2017) Feature based local binary patterns for rotation invariant texture classification. Expert Syst Appl 88:238–248. https://doi.org/10.1016/j.eswa.2017.07.007

    Article  Google Scholar 

  34. Xu X, Li Y, Wu QMJ (2020) A completed local shrinkage pattern for texture classification. Appl Soft Comput 97(B):106830. https://doi.org/10.1016/j.asoc.2020.106830

    Article  Google Scholar 

  35. Xu X, Li Y, Wu QMJ (2021) A compact multi-pattern encoding descriptor for texture classification. Dig Signal Process 114(2):103081. https://doi.org/10.1016/j.dsp.2021.103081

    Article  Google Scholar 

  36. Pan Z, Hu S, Wu X, Wang P (2021) Adaptive center pixel selection strategy to local binary pattern for texture classification. Expert Syst Appl 180(4):115–123. https://doi.org/10.1016/j.eswa.2021.115123

    Article  Google Scholar 

  37. Mallat S (1989) A theory of multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell 11(4):674–693. https://doi.org/10.1109/34.192463

    Article  Google Scholar 

  38. Tymczak C, Niklasson A, Roder H (2002) Separable and nonseparable multiwavelets in multiple dimensions. J Comput Phys 175(2):363–397. https://doi.org/10.1006/jcph.2001.6743

    Article  MathSciNet  Google Scholar 

  39. Feauveau JC (1990) Analyse Multirésolution Par Ondelettes Nonorthogonale et Benes de Filtres Numériques. PhD. Thesis, Université de Paris Sud

  40. Kovačević J, Vetterli M (1991) Nonseparable multidimensional perfect reconstruction filter banks and wavelet bases for \(\mathbb{R} ^n\). IEEE Trans Inf Theory 38(2):533–555

    Article  Google Scholar 

  41. Cohen A, Daubechies I (1993) Non-separable bidimensional wavelet bases. Revista Matematica Iberoamericana 9(1):51–137. https://doi.org/10.4171/RMI/133

    Article  MathSciNet  Google Scholar 

  42. Wouwer GVD, Scheunders P, Dyck D (1998) Rotation-invariant texture characterization using isotropic wavelet frames. Int Conf Pattern Recognit IEEE 1:814–816. https://doi.org/10.1109/ICPR.1998.711273

    Article  Google Scholar 

  43. Movassagh AA, Alzubi J, Gheisari M, Rahimi M, Mohan S, Abbasi A, Nabipour N (2021) Artificial neural networks training algorithm integrating invasive weed optimization with diferential evolutionary model. J Ambient Intell Hum Comput 14. https://doi.org/10.1007/s12652-020-02623-6

  44. Alzubi O, Qiqieh I, Alzubi J (2022) Fusion of deep learning based cyberattack detection and classification model for intelligent systems. Cluster Comput 26. https://doi.org/10.1007/s10586-022-03686-0

  45. Alzubi O, Alzubi J, Alweshah M, Qiqieh I, Al-Shami S, Manikandan R (2020) An optimal pruning algorithm of classifier ensembles: dynamic programming approach. Neural Comput Appl 32:1–17. https://doi.org/10.1007/s00521-020-04761-6

    Article  Google Scholar 

  46. Alzubi J (2015) Optimal classifier ensemble design based on cooperative game theory. Res J Appl Sci Eng Technol 11(12):1336–1346. https://doi.org/10.19026/rjaset.11.2241

    Article  Google Scholar 

  47. Kylberg G (2011) The kylberg texture dataset. Centre for Image Analysis, Swedish University of Agricultural Sciences and Uppsala University, External report (Blue series) No. 35

  48. Xu Y, Yang X, Ling H, Ji H (2010) A new texture descriptor using multifractal analysis in multi-orientation wavelet pyramid. In: The 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 161–168 . https://doi.org/10.1109/CVPR.2010.5540217

  49. Lazebnik S, Schmid C, Ponce J (2005) A sparse texture representation using local affine regions. IEEE Trans Pattern Anal Mach Intell 27(8):1265–1278. https://doi.org/10.1109/TPAMI.2005.151

    Article  Google Scholar 

  50. 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 

  51. Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987. https://doi.org/10.1109/TPAMI.2002.1017623

    Article  Google Scholar 

  52. Dai X, Diao Y, Xin J (2010) Multipliers, phases and connectivity of MRA wavelets in \({L}^2(\mathbb{R} ^2)\). J Fourier Anal Appl 16:155–176. https://doi.org/10.1007/s00041-009-9089-6

    Article  MathSciNet  Google Scholar 

  53. Lagarias J, Wang Y (1998) Haar type orthonormal wavelet basis in \(\mathbb{R} ^2\). J Fourier Anal Appl 2(1):1–14

    Article  Google Scholar 

  54. Daubechies I (1992) Ten Lecture on Wavelets, vol 61. Society for Industrial and Applied, SIAM, Philadelphia

    Book  Google Scholar 

  55. Meyer Y (1995) Wavelets and Operators vol. 37. Cambridge Studies in Advanced Mathematics, Cambridge University Press, Cambridge

  56. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297

    Article  Google Scholar 

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Acknowledgements

This work was supported by National Key R &D Program of China (2020YFB1707802), National Natural Science Foundation of China (No.12071131, No.11571107), a project funded by the China Postdoctoral Science Foundation (Grant No. 2020M680480) and Natural Science Foundation of Hebei Province, China (F2021201020). The authors thank the anonymous referees for their helpful comments and suggestions that helped us improve the presentation of this paper.

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Authors and Affiliations

  1. School of Control and Computer Engineering, North China Electric Power University, Beijing, 102206, China

    Han Su & Jiankai Chen

  2. School of Mathematics and Physics, North China Electric Power University, Beijing, 102206, China

    Zhongyan Li & Huixian Meng

  3. Hebei Key Laboratory of Machine Learning and Computational Intelligence, College of Mathematics and Information Science, Hebei University, Baoding, 071002, China

    Jiankai Chen & Xin Wang

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  1. Han Su
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Su, H., Chen, J., Li, Z. et al. The fusion feature wavelet pyramid based on FCIS and GLCM for texture classification. Int. J. Mach. Learn. & Cyber. 15, 1907–1926 (2024). https://doi.org/10.1007/s13042-023-02005-2

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