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FFT Consolidated Sparse and Collaborative Representation for Image Classification

  • Research Article - Computer Engineering and Computer Science
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Abstract

Spectrum analysis can quickly extract and analyze frequency domain features of signal, and it has been widely applied in fields of image processing, noise processing and signal processing. Fast Fourier transform (FFT) is fast and efficient, because it can efficiently decrease complexity of discrete Fourier transform. As a consequence, FFT is a very good method for image frequency spectrum analysis. In this paper, we propose to consolidate frequency domain representation by sparse representation (SR) and collaborative representation classification (CRC) which has excellent performance in comparison with general sparse representation-associated classification algorithms. Our proposed novel method has three main phases. The first phase utilizes FFT to extract frequency domain features of original images, which are complementary with representations of the original images. The second phase of our proposed novel method exploits CRC or SR to obtain scores of original images and obtained features, respectively. The third phase integrates the scores of original images and obtained features and uses them to classify images. The major contribution of the proposed method is that it is usually more robust than methods using only FFT, CRC or state-of-art method CIRLRC for image classification. The experiments of image classification demonstrate that the simultaneous use of FFT and CRC or sparse representation classification has high accuracy on image recognition.

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References

  1. Chen, Z.X.; Liu, C.Y.; Chang, F.L.; et al.: Fast face detection algorithm based on improved skin-color model. Arab. J. Sci. Eng. 38(3), 629–635 (2013)

    Article  Google Scholar 

  2. Liu, Z.; Zhang, Z.: Face Geometry and Appearance Modeling: Concepts and Applications, CUP (2011)

  3. Turk, M.; Pentland, A.: Eigenfaces for recognition. J. Cognit. Neurosci. 3(1), 71–86 (1991)

    Article  Google Scholar 

  4. Yang, J.; Zhang, D.; Frangi, Alejandro F.; Yang, J.Y.: Two-dimensional PCA: a new approach to appearance-based face representation and recognition. IEEE Trans. Pattern Anal. Mach. Intell. 26(1), 131–137 (2004)

    Article  Google Scholar 

  5. Huang, S.; Ye, J.; Wang, T.; et al.: Extracting refined low-rank features of robust PCA for human action recognition. Arab. J. Sci. Eng. 40(5), 1427–1441 (2015)

    Article  Google Scholar 

  6. Li, M.; Yuan, B.: 2D-LDA: a statistical linear discriminant analysis for image matrix. Pattern Recognit. Lett. 26(5), 527–532 (2005)

    Article  Google Scholar 

  7. Zhang, L.; Yang, M.; Feng, X.: Sparse representation or collaborative representation: Which helps face recognition? In: Proceedings of IEEE Conference on Computer Vision, pp. 471–478 (2011)

  8. Zhang, Z.; Xu, Y.; Yang, J.; et al.: A survey of sparse representation: algorithms and applications. IEEE Access. 3, 490–530 (2015)

    Article  Google Scholar 

  9. Wright, J.; Yang, A.Y.; Ganesh, A.; et al.: Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 31(2), 210–227 (2009)

    Article  Google Scholar 

  10. Xu, Y.; Zhang, B.; Zhong, Z.: Multiple representations and sparse representation for image classification. Pattern Recognit. Lett. 68, 9–14 (2015)

    Article  Google Scholar 

  11. Xu, Y.; Fang, X.; Li, X.; Yang, J.; You, J.; Liu, H.; Teng, S.: Data uncertainty in face recognition. IEEE Trans. Cybern. 44(10), 1950–1961 (2014)

    Article  Google Scholar 

  12. Li, W.; Du, Q.; Zhang, B.: Combined sparse and collaborative representation for hyperspectral target detection. Pattern Recognit. 48(12), 3904–3916 (2015)

    Article  Google Scholar 

  13. Nandakumar, K.; Chen, Y.; Dass, S.C.; et al.: Likelihood ratio-based biometric score fusion. IEEE Trans. Pattern Anal. Mach. Intell. 30(2), 342–347 (2008)

    Article  Google Scholar 

  14. Ma, Z.; Wen, J.; Liu, Q.; et al.: Near-infrared and visible light image fusion algorithm for face recognition. J. Mod. Opt. 62(9), 745–753 (2015)

  15. Goel, T.; Nehra, V.; Vishwakarma, V.P.: An adaptive non-symmetric fuzzy activation function-based extreme learning machines for face recognition. Arab. J. Sci. Eng. 42(2), 805–816 (2017)

  16. Lu, C.Y.; Min, H.; Gui, J.; et al.: Face recognition via weighted sparse representation. J. Vis. Commun. Image Represent. 24(2), 111–116 (2013)

    Article  Google Scholar 

  17. Xu, Y.; Zhu, Q.; Fan, Z.; et al.: Using the idea of the sparse representation to perform coarse-to-fine face recognition. Inf. Sci. 238, 138–148 (2013)

    Article  MathSciNet  Google Scholar 

  18. Feng, Q.; Zhu, Q.; Tang, L.L.; et al.: Double linear regression classification for face recognition. J. Mod. Opt. 62(4), 288–295 (2015)

    Article  Google Scholar 

  19. Melin, P.; Mendoza, O.; Castillo, O.: Face recognition with an improved interval type-2 fuzzy logic sugeno integral and modular neural networks. IEEE Trans. Sys. Man. Cybern. 41(5), 1001–1012 (2011)

    Article  Google Scholar 

  20. Xu, Y.; Li, X.; Yang, J.; et al.: Integrate the original face image and its mirror image for face recognition. Neurocomputing 131, 191–199 (2014)

  21. Ishizuka, K.: A practical approach for STEM image simulation based on the FFT multislice method. Ultramicroscopy 90(2), 71–83 (2002)

    Article  Google Scholar 

  22. Fialka, O.; Čadik, M.: FFT and convolution performance in image filtering on GPU. In: Proceedings of IEEE Conference on Information Visualization, pp. 609–614 (2006)

  23. Ross, A.; Jain, A.: Information fusion in biometrics. Pattern Recognit. Lett. 24(13), 2115–2125 (2003)

    Article  Google Scholar 

  24. Sarhan, S.; Alhassan, S.; Elmougy, S.: Multimodal biometric systems: a comparative study. Arab. J. Sci. Eng. 42(2), 443–457 (2017)

  25. Zhang, D.; Song, F.X.; et al.: Advanced Pattern Recognition Technologies with Applications to Biometrics. Medical Information Science Reference, New York (2009)

    Book  Google Scholar 

  26. Xu, Y.; Zhang, D.: Represent and fuse bimodal biometric images at the feature level: complex-matrix-based fusion scheme. Opt. Eng. 49(3), 037002-1–037002-6 (2010)

  27. Jain, A.; Nandakumar, K.; Ross, A.: Score normalization in multimodal biometric system. Pattern Recognit. 38(12), 2270–2285 (2005)

    Article  Google Scholar 

  28. Beck, A.; Teboulle, M.: A fast iterative shrinkage-thresholding algorithm with application to wavelet-based image deblurring, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, pp. 693–696 (2009)

  29. Xu, Y.; Li, X.; Yang, J.; et al.: Integrating conventional and inverse representation for face recognition. IEEE Trans. Cybern. 44(10), 1738–1746 (2014)

    Article  Google Scholar 

  30. Zhang, B.L.; Zhang, H.; Ge, S.S.: Face recognition by applying wavelet subband representation and kernel associative memory. IEEE Trans. Neural Netw. 15(1), 166–177 (2004)

    Article  Google Scholar 

  31. Gong, P.; Zhang, C.; Lu, Z. et al.: A general iterative shrinkage and thresholding algorithm for non-convex regularized optimization problems. In: Proceedings of IEEE conference on machine learning, vol. 28, no. 2, 37–45 (2013)

  32. https://en.wikipedia.org/wiki/Lagrange_multiplier

  33. http://www.anefian.com/face_reco.htm

  34. http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html

  35. http://www.yongxu.org/databases.html

  36. http://www2.ece.ohio-state.edu/~aleix/ARdatabase.html

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Authors and Affiliations

  1. School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, 150080, China

    Chunwei Tian, Guanglu Sun & Zhichao Song

  2. School of Economics and Management, Northeast Agriculture University, Harbin, 150030, China

    Qi Zhang

  3. School of Foreign Languages, Harbin University of Science and Technology, Harbin, 150080, China

    Siyan Li

Authors
  1. Chunwei Tian
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  2. Qi Zhang
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  3. Guanglu Sun
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  4. Zhichao Song
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  5. Siyan Li
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Correspondence to Chunwei Tian.

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Tian, C., Zhang, Q., Sun, G. et al. FFT Consolidated Sparse and Collaborative Representation for Image Classification. Arab J Sci Eng 43, 741–758 (2018). https://doi.org/10.1007/s13369-017-2696-7

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  • DOI: https://doi.org/10.1007/s13369-017-2696-7

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