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A new sparse representation-based classification algorithm using iterative class elimination

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Abstract

In this paper, we propose a sparse representation-based classification method using iterative class elimination strategy for face recognition. The proposed method aims to represent a test sample as a linear combination of the most competitive training samples and exploits an optimal representation of training samples from the classes with major relevant contributions. We interpret the sparse representation problem as an information fidelity problem. In the context of our proposed method, an important goal is to select a subset of variables for accomplishing one objective: the provision of a descriptive representation for sparse class knowledge structure. We develop an iterative class elimination algorithm to achieve this goal. First, the contribution in presenting the test sample of any of the specified classes is, respectively, calculated by adding up the total contribution of all training samples of this class, and then a certain class that meets the smallest score requirement to this test sample is eliminated. Second, a similar procedure is iteratively carried out for the set of remaining training samples from rest classes, and this procedure is repeatedly performed till the predefined termination condition is satisfied. The final remaining training samples are used to produce a best representation of the test sample and to classify it. Therefore, the proposed algorithm is an iterative method that alternates between sparse representation and a process of updating the training atoms to better fit the test data. This is helpful to accurately classify the test sample. Experimental results conducted on the ORL, FERET, and AR face databases demonstrate the effectiveness of the proposed method.

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References

  1. Mazhar R, Gader PD, Wilson JN (2008) A matching pursuit based similarity measure for fuzzy clustering and classification of signals. In: International conference on fuzzy systems, Hong Kong

  2. Moghadam AE, Shirani S (2007) Matching pursuit-based region-of-interest image coding. IEEE Trans Image Process 16(2):406–415

    Article  MathSciNet  Google Scholar 

  3. Marcellin, MW, Gormish MJ, Bilgin A, Boliek MP (2000) An overview of JPEG-2000, In: Proceedings of data compression conference, pp 523–541

  4. Starck JL, Candes EJ, Donoho DL (2002) The curvelet transform for image denoising. IEEE Trans Image Process 11:670–684

    Article  MathSciNet  MATH  Google Scholar 

  5. Elad M, Starck JL, Querre P (2005) Simultaneous cartoon and texture image in painting using morphological component analysis. J Appl Comput Harmon Anal 19:340–358

    Article  MathSciNet  MATH  Google Scholar 

  6. Kwak KC, Pedrycz W (2005) Face recognition using a fuzzy fisherface classifier. Pattern Recogn 38(10):1717–1732

    Article  Google Scholar 

  7. Elad M (2010) Sparse and redundant representations: from theory to applications in signal and image processing. Springer, Berlin

    Book  Google Scholar 

  8. Huang K, Aviyente S (2006) Sparse representation for signal classification. In: Proceedings of neural information processing systems

  9. Rodriguez F, Sapiro G (2008) Sparse representation for image classification: learning discriminative and reconstructive non-parametric dictionaries. IMA Preprint Series #2213

  10. Wright J, Yang AY, Ganesh A, Sastry SS, Ma Y (2009) Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell 31(2):210–227

    Article  Google Scholar 

  11. Gao SH, Tsang IW-H, Chia L-T (2010) Kernel sparse representation for image classification and face recognition. In: Proceedings of european conference computer vision

  12. Yang M, Zhang L (2010) Gabor feature based sparse representation for face recognition with Gabor occlusion dictionary. In: Proceedings of European conference computer vision

  13. Cheng B, Yang J, Yan S, Fu Y, Huang T (2010) Learning with l 1-graph for image analysis. IEEE Trans Image Process 19(4):858–866

    Article  MathSciNet  Google Scholar 

  14. Qiao LS, Chen SC, Tan XY (2010) Sparsity preserving projections with applications to face recognition. Pattern Recogn 43(1):331–341

    Article  MATH  Google Scholar 

  15. Yang J, Yu K, Gong Y, Huang T (2009) Linear spatial pyramid matching using sparse coding for image classification. In: CVPR, pp 1794–1801

  16. Zhang L, Yang M, Feng X, Ma Y, Zhang D (2012) Collaborative representation based classification for face recognition, CoRR abs/1204.2358

  17. Xu Y, Zhang D, Yang J, Yang JY (2011) A two-phase test sample sparse representation method for use with face recognition. IEEE Trans Circuits Syst Video Technol 21(9):1255–1262

    Article  Google Scholar 

  18. Xu Y, Zuo WM, Fan ZZ (2012) Supervised sparse representation method with a heuristic strategy and face recognition experiments. Neurocomputing 79:125–131

    Article  Google Scholar 

  19. Xu Y, Zhong A, Yang J, Zhang D (2011) Bimodal biometrics based on a representation and recognition approach. Opt Eng 50(3):037202

    Article  Google Scholar 

  20. Xu Y, Zhu Q, Zhang D, Yang J-Y (2011) Combine crossing matching scores with conventional matching scores for bimodal biometrics and face and palmprint recognition experiments. Neurocomputing 74:3946–3952

    Article  Google Scholar 

  21. Vapnik V (1998) Statistical learning theory. Wiley, NewYork

    MATH  Google Scholar 

  22. Mary-Huard T, Robin S, Daudin JJ (2007) A penalized criterion for variable selection in classification. J Multivar Anal 98:695–705

    Article  MathSciNet  MATH  Google Scholar 

  23. Xu Y, Zhang D, Yang J, Yang JY (2008) An approach for directly extracting features from matrix data and its application in face recognition. Neurocomputing 71:1857–1865

    Article  Google Scholar 

  24. Xu Y, Zhang D, Yang JY (2010) A feature extraction method for use with bimodal biometrics. Pattern Recogn 43:1106–1115

    Article  MATH  Google Scholar 

  25. Song XN, Yang JY, Wu XJ, Yang XB (2011) An optimal symmetrical null space criterion of fisher discriminant for feature extraction and recognition. Soft Comput 15:281–293

    Article  MATH  Google Scholar 

  26. Song XN, Zheng YJ, Wu XJ, Yang XB, Yang JY (2010) A complete fuzzy discriminant analysis approach for face recognition. Appl Soft Comput 10:208–214

    Article  Google Scholar 

  27. Cevikalp H, Neamtu M, Wilkes M, Barkana A (2005) Discriminative common vectors for face recognition. IEEE Trans Pattern Anal Mach Intell 27(1):914–919

    Article  Google Scholar 

  28. Zhou X, Huang T (2001) Small sample learning during multimedia retrieval using biasmap. In: Proceedings of the IEEE international conference on computer vision. pp 11–17

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

    Article  Google Scholar 

  30. Xu Y, Zhang D, Song FX et al (2007) A method for speeding up feature extraction based on KPCA. Neurocomputing 70:1056–1061

    Article  Google Scholar 

  31. Peng H, Long F, Ding C (2005) Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27(8):1226–1238

    Article  Google Scholar 

  32. Chow T, Huang D (2005) Estimating optimal feature subsets using efficient estimation of high-dimensional mutual information. IEEE Trans Neural Netw 16(1):213–224

    Article  MathSciNet  Google Scholar 

  33. ORL database, Available: http://www.cl.cam.ac.uk/research/dtg/attarchive/ facedatabase.html

  34. FERET database, Available: http://www.itl.nist.gov/iad/humanid/feret/feret-master.html

  35. AR database, Available: http://cobweb.ecn.purdue.edu/aleix/aleix-face-DB.html

  36. Xu Y, Jin Z (2008) Down-sampling face images and low-resolution face recognition. In: Proceedings of the third international conference on innovative computing, information and control. pp 392–395

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Acknowledgments

This work was supported by the National Science Foundation of China (Grant No. 61100116), the Natural Science Foundation of Jiangsu Province (Grant Nos. BK2012700 and BK2011492), the China Postdoctoral Science Foundation (Grant No. 2011M500926), the Jiangsu Postdoctoral Science Foundation (Grant No. 1102063C), the Foundation of Artificial Intelligence Key Laboratory of Sichuan Province (Grant No. 2012RZY02) and the Foundation of Key Laboratory of Intelligent Computing and Signal Processing, Ministry of Education, Anhui University.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Jiangsu University of Science and Technology, Zhenjiang, 212003, People’s Republic of China

    Xiaoning Song, Xibei Yang & Shang Gao

  2. School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, 210094, People’s Republic of China

    Xiaoning Song, Zi Liu, Xibei Yang & Shang Gao

  3. Key Laboratory of Intelligent Computing and Signal Processing, Ministry of Education, Anhui University, Hefei, 230039, People’s Republic of China

    Xiaoning Song

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  1. Xiaoning Song
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  2. Zi Liu
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  4. Shang Gao
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Correspondence to Xiaoning Song.

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Song, X., Liu, Z., Yang, X. et al. A new sparse representation-based classification algorithm using iterative class elimination. Neural Comput & Applic 24, 1627–1637 (2014). https://doi.org/10.1007/s00521-013-1399-6

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