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Pattern Recognition and Image Analysis

, Volume 28, Issue 1, pp 59–70 | Cite as

Bi-dimensional Empirical Mode Decomposition and Nonconvex Penalty Minimization L q (q = 0.5) Regular Sparse Representation-based Classification for Image Recognition

  • Qing Li
  • Xia Ji
  • S. Y. Liang
Representation, Processing, Analysis, and Understanding of Images

Abstract

This paper reports an innovative pattern recognition technique for fracture microstructure images based on Bi-dimensional empirical mode decomposition (BEMD) and nonconvex penalty minimization L q (q = 0.5) regular sparse representation-based classification (NPMLq-SRC) algorithm. The detailed procedures of this work can be divided into three steps, i.e., the preprocessing stage, the feature extraction stage and the image classification stage. We test and validate the proposed method through real data from metallic alloy fracture images. The case verification results show that our proposal can obtain a much higher recognition accuracy than the conventional Back Propagation Neural Networks (BPNN for short), the L1-norm minimization sparse representation-based classification (L1-SRC) and the BEMD combined with L1-norm minimization sparse representation-based classification (BEMD+L1-SRC) methods, respectively. Specifically, the proposed BEMD+NPMLq-SRC (q = 0.5) method outperforms the BEMD+L1-SRC method by 3.33% improvement of the average recognition accuracy, and outperforms L1-SRC method by 14.06% improvement of the average recognition accuracy, respectively.

Keywords

images recognition fracture microstructure images (FMI) BEMD method feature extraction NPMLq-SRC compressed sensing 

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References

  1. 1.
    G. R. Fu, Y. J. Tian, F. J. Lv, and Q. P. Zhong, “Fracture reasons investigation of turning rack component in vehicle,” Eng. Fail. Anal. 16 (1), 484–494 (2009).CrossRefGoogle Scholar
  2. 2.
    M. A. Lucas, A. L. Chinelatto, E. C. Grzebielucka, E. Prestes, and L. A. D. Lacerda, “Analytical fractal model for rugged fracture surface of brittle materials,” Eng. Fract. Mech. 162, 232–255 (2016).CrossRefGoogle Scholar
  3. 3.
    T. W. J. de Geus, R. H. J. Peerlings, and M. G. D. Geers, “Microstructural modeling of ductile fracture initiation in multi-phase materials,” Eng. Fract. Mech. 147, 318–330 (2015).CrossRefGoogle Scholar
  4. 4.
    K. Komai, K. Minoshima, and S. Ishji, “Recognition of different fracture surface morphologies using computer image processing technique,” Int. J. Ser. A, Mech. Mater. Eng. 36 (2), 220–227 (1993).Google Scholar
  5. 5.
    K. Minoshima, T. Nagasaki, and K. Komai, “Automatic classification of fracture surface morphology using computer image processing technique,” Jpn. Soc. Mech. Eng. 56 (625), 1319–1323 (1990).Google Scholar
  6. 6.
    Z. N. Li, Y. Sun, J. W. Yan, S. R. Long, and Y. C. Yang, “Study on the recognition method of metal fracture images based on Grouplet-RVM,” Chn. J. Sci. Instrum. 35 (6), 1347–1353 (2014).Google Scholar
  7. 7.
    Y. M. Niu, Y. S. Wong, G. S. Hongand, and T. I. Liu, “Multi-category classification of tool conditions using wavelet packets and ART2 network,” J. Manuf. Sci. Eng. Trans. ASME 120 (4), 807–816 (1998).CrossRefGoogle Scholar
  8. 8.
    Y. H. Yan, J. H. Gao, Y. Liu, Y. G. Cao, and S. C. Lei, “Recognition and classification of metal fracture surface models based on wavelet transform,” Acta Metall. Sin. 38 (2), 309–314 (2002).Google Scholar
  9. 9.
    K. Yamagiwa, S. Izumi, and S. Sakai, “Detecting method of striation region of fatigue fracture surface using wavelet transform,” J. Soc. Mater. Sci. Jpn. 53, 306–312 (2004).CrossRefGoogle Scholar
  10. 10.
    D. L. Donoho, “Compressed Sensing,” IEEE Trans. Inform. Theory 52 (4), 1289–1306 (2006).MathSciNetCrossRefMATHGoogle Scholar
  11. 11.
    E. J. Candes, J. Romberg, and T. Tao, “Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information,” IEEE T. Inform. Theory 52 (2), 489–509 (2006).MathSciNetCrossRefMATHGoogle Scholar
  12. 12.
    Y. Tsaig and D. L. Donoho, “Extensions of compressed sensing,” Signal Process. 86 (3), 549–571 (2006).CrossRefMATHGoogle Scholar
  13. 13.
    J. Wright, Y. Ma, J. Mairal, G. Sapiro, T. S. Huang, and S. Yan, “Sparse representation for computer vision and pattern recognition,” Proc. IEEE 98, 1031–1044 (2009).CrossRefGoogle Scholar
  14. 14.
    J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, “Robust face recognition via sparse representation,” IEEE Trans. Pattern Anal. 31, 210–227 (2009).CrossRefGoogle Scholar
  15. 15.
    H. Z. Zhang, F. Q. Wang, Y. Chen, W. D. Zhang, K. Q. Wang, and J. D. Liu. “Sample pair based sparse representation classification for face recognition,” Expert Syst. Appl. 45 (1), 352–358 (2016).CrossRefGoogle Scholar
  16. 16.
    Z. P. Hu, F. Bai, S. H. Zhao, M. Wang, and Z. Sun, “Extended common molecular and discriminative atom dictionary based sparse representation for face recognition,” J. Vis. Commun. Image R. 40, 42–50 (2016).CrossRefGoogle Scholar
  17. 17.
    B. D. Liu, B. Shen, L. K. Gui, Y. X. Wang, X. Li, F. Yan, and Y. J. Wang, “Face recognition using class specific dictionary learning for sparse representation and collaborative representation,” Neurocomputing 204 (5), 198–210 (2016).CrossRefGoogle Scholar
  18. 18.
    Y. H. Wang, J. Q. Qiao, J. B. Li, P. Fu, S. C. Chu, and J. F. Roddick, “Sparse representation-based MRI super-resolution reconstruction,” Measurement 47, 946–953 (2014).CrossRefGoogle Scholar
  19. 19.
    X. Y. Zhao, Z. X. He, S. Y. Zhang, and D. Liang, “Robust pedestrian detection in thermal infrared imagery using a shape distribution histogram feature and modified sparse representation classification,” Pattern Recogn. 48 (6), 1947–1960 (2015).CrossRefGoogle Scholar
  20. 20.
    S. I. Wright, M. M. Nowell, S. P. Lindeman, P. P. Camus, M. D. Graefc, and M. A. Jackson, “Introduction and comparison of new EBSD post-processing methodologies,” Ultramicroscopy 159, 81–94 (2015).CrossRefGoogle Scholar
  21. 21.
    S. I. Wright, “Random thoughts on non-random misorientation distributions,” Mater. Sci. Technol. 22, 1287–1296 (2006).CrossRefGoogle Scholar
  22. 22.
    J. Chen and Z. Yi, “Sparse representation for face recognition by discriminative low rank matrix recovery,” J. Vis. Commun. Image R. 25, 763–773 (2014).CrossRefGoogle Scholar
  23. 23.
    Z. Jiang, Z. Lin, and L.S. Davis, “Label consistent KSVD: Learning a discriminative dictionary for recognition,” IEEE Trans. Pattern Anal. Mach. Intell. 35, 2651–2664 (2013).CrossRefGoogle Scholar
  24. 24.
    C. P. Wei, Y. W. Chao, Y. R. Yeh, and Y. C. F. Wang, “Locality-sensitive dictionary learning for sparse representation based classification,” Pattern Recogn. 46, 1277–1287 (2013).CrossRefMATHGoogle Scholar
  25. 25.
    J. W. Tao, S. T. Wen, and W. J. Hu, “Robust domain adaptation image classification via sparse and low rank representation,” J. Vis. Commun. Image R. 33, 134–148 (2015).CrossRefGoogle Scholar
  26. 26.
    Y. X. Sun and G. H. Wen, “Adaptive feature transformation for classification with sparse representation,” Optik 126, 4452–4459 (2015).CrossRefGoogle Scholar
  27. 27.
    J. H. Wang, H. Z. Liu, and N. He, “Exposure fusion based on sparse representation using approximate KSVD,” Neurocomputing 135 (5), 145–154 (2014).CrossRefGoogle Scholar
  28. 28.
    X. Q. Lu, Y. L. Wang, and Y. Yuan, “Graph-regularized low-rank representation for destriping of hyper spectral images,” IEEE Trans. Geosci. Rem. Sens. 51 (7), 4009–4018 (2013).CrossRefGoogle Scholar
  29. 29.
    C. H. Zheng, Y. F. Hou, and J. Zhang, “Improved sparse representation with low-rank representation for robust face recognition,” Neurocomputing 198 (19), 114–124 (2016).CrossRefGoogle Scholar
  30. 30.
    J. C. Nunes, Y. Bouaoune, E. Delechelle, O. Niang, and P. H. Bunel, “Image analysis by bidimensional empirical mode decomposition,” Image Vision Comput. 21, 1019–1026 (2003).CrossRefMATHGoogle Scholar
  31. 31.
    J. C. Nunes, S. Guyot, and E. Delechelle, “Texture analysis based on local analysis of the bidimensional empirical model decomposition,” J. Mach. Vision Appl. 16 (3), 177–188 (2005).CrossRefGoogle Scholar
  32. 32.
    R. M. Haralick, “Textural features for image classification,” IEEE Trans. Syst. Man Cybern. 3, 610–621 (1973).MathSciNetCrossRefGoogle Scholar
  33. 33.
    R. M. Haralick, “Computer classification of reservoir sandstones,” IEEE Trans. Geosci. Electron. 11, 171–177 (1973).CrossRefGoogle Scholar
  34. 34.
    M. Costa, A. L. Goldberger, and C. K. Peng, “Multiscale entropy analysis of complex physiologic time series,” Phys. Rev. Lett. 89 (6), 068102 (2002).CrossRefGoogle Scholar
  35. 35.
    J. S. Richman and J. R. Moorman, “Physiological time-series analysis using approximate entropy and sample entropy,” Am. J. Physiol. Heart Circ. Physiol. 278, 2039–2049 (2000).CrossRefGoogle Scholar
  36. 36.
    D. E. Lake, J. S. Richman, M. P. Griffin, and J. R. Moorman, “Sample entropy analysis of neonatal heart rate variability,” Am. J. Physiol. Regul. Integr. Comp. Physiol. 283, 789–797 (2002).CrossRefGoogle Scholar
  37. 37.
    Y. Liu, L. Y. Chen, H. M. Wang, L. L. Jiang, Y. Zhang, J. F. Zhao, D. Y. Wang, Y. C. Zhao, and Y. C. Song, “An improved differential box-counting method to estimate fractal dimensions of gray-level images,” J. Vis. Commun. Image R. 25, 1102–1111 (2014).CrossRefGoogle Scholar
  38. 38.
    J. Li, Q. Du, and C. X. Sun, “An improved box-counting method for image fractal dimension estimation,” Pattern Recogn. 42, 2460–2469 (2009).CrossRefMATHGoogle Scholar
  39. 39.
    E.J. Candes and J. Romberg, “Sparsity and incoherence in compressive sampling,” Inverse Probl. 23 (3), 969–985 (2007).MathSciNetCrossRefMATHGoogle Scholar
  40. 40.
    E. J. Candes and T. Tao, “Near-optimal signal recovery from random projections: universal encoding strategies?,” IEEE Trans. Inform. Theory 52, 5406–5425 (2006).MathSciNetCrossRefMATHGoogle Scholar
  41. 41.
    E. J. Candes, “The restricted isometry property and its implications for compressed sensing,” C. R. Math. Acad. Sci. Paris 346 (9–10), 589–592 (2008).MathSciNetCrossRefMATHGoogle Scholar
  42. 42.
    B. K. Natarajan, “Sparse approximate solutions to linear systems,” SIAM J. Comput. 24 (2), 227–234 (1995).MathSciNetCrossRefMATHGoogle Scholar
  43. 43.
    C. Li, S. H. Ying, B. L. Shen, S. Y. Qiu, X. Y. Ling, Y. F. Wang, and Q. Peng, “Cyclic stress-strain response of textured Zircaloy-4,” J. Nucl. Mater. 321, 60–69 (2003).CrossRefGoogle Scholar
  44. 44.
    W. Wang, C. Chen, and K. N. Michael, “An image pixel based variational model for histogram equalization,” J. Vis. Commun. Image R. 34, 118–134 (2016).CrossRefGoogle Scholar
  45. 45.
    N. B. Nagaraj, K. Kanchan, D. Samik, K. P. Surjya, and P. Srikanta, “Friction stir weld classification by applying wavelet analysis and support vector machine on weld surface images,” J. Manuf. Process. 20, 274–281 (2015).CrossRefGoogle Scholar

Copyright information

© Pleiades Publishing, Ltd. 2018

Authors and Affiliations

  1. 1.College of Mechanical EngineeringDonghua UniversityShanghaiP. R. China
  2. 2.George W. Woodruff School of Mechanical EngineeringGeorgia Institute of TechnologyAtlantaUSA

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