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Virtual Journal for Biomedical Optics

Virtual Journal for Biomedical Optics


  • Editors: Andrew Dunn and Anthony Durkin
  • Vol. 8, Iss. 8 — Sep. 4, 2013

Dissimilarity sparsity-preserving projections in feature extraction for visual recognition

Fengtao Xiang, Zhengzhi Wang, and Xingsheng Yuan  »View Author Affiliations

Applied Optics, Vol. 52, Issue 20, pp. 5022-5029 (2013)

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This paper investigates the use of feature dimensionality reduction approaches for high-dimensional data analysis. Most of the existing preserving projection methods are based on similarity, such as the well-known locality-preserving projections, neighborhood-preserving embedding, and sparsity-preserving projections. Here, we propose a simple yet very efficient preserving projection method based on sparsity and dissimilarity for feature extraction, named dissimilarity sparsity-preserving projections, which is an extended version of sparsity-preserving projections. Both projection coefficients and reconstructive residuals are considered in our proposed framework. We give an idea of a “dissimilarity metric” as the measurement of the relationship among the object data. If the value of the dissimilarity metric of two samples is large, the possibility of them belonging to the same class is small. The proposed methods do not have to preset the number of neighbors and heat kernel width, which is one of the important differences from other projection methods. In practical applications, an approximately direct and complete solution is obtained for the proposed algorithm. Experimental results on three widely used face datasets demonstrate that the proposed framework could achieve competitive performance in terms of accuracy and efficiency.

© 2013 Optical Society of America

OCIS Codes
(070.5010) Fourier optics and signal processing : Pattern recognition
(100.0100) Image processing : Image processing
(100.2000) Image processing : Digital image processing
(100.5010) Image processing : Pattern recognition
(100.3008) Image processing : Image recognition, algorithms and filters

ToC Category:
Image Processing

Original Manuscript: March 18, 2013
Revised Manuscript: June 5, 2013
Manuscript Accepted: June 5, 2013
Published: July 10, 2013

Virtual Issues
Vol. 8, Iss. 8 Virtual Journal for Biomedical Optics

Fengtao Xiang, Zhengzhi Wang, and Xingsheng Yuan, "Dissimilarity sparsity-preserving projections in feature extraction for visual recognition," Appl. Opt. 52, 5022-5029 (2013)

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  1. A. K. Jain, R. P. W. Duin, and J. Mao, “Statistical pattern recognition: a review,” IEEE Trans. Pattern Anal. Mach. Intell. 22, 4–37 (2000). [CrossRef]
  2. L. Qiao, S. Chen, and X. Tan, “Sparsity preserving projections with applications to face recognition,” Pattern Recogn. 43, 331–341 (2010). [CrossRef]
  3. L. K. Saul and S. T. Roweis, “Think globally, fit locally: unsupervised learning of low dimensional manifolds,” J. Mach. Learn. Res. 4, 119–155 (2003). [CrossRef]
  4. J. T. Jolloffe, Principal Component Analysis (Springer-Verlag, 1986).
  5. X. He and P. Niyogi, “Locality preserving projections,” Adv. Neural Inf. Process. Syst. 16, 1–8 (2003).
  6. X. He, D. Cai, S. Yan, and H.-J. Zhang, “Neighborhood preserving embedding,” in Tenth IEEE International Conf. on Computer Vision, Beijing, China (2005), pp. 1–8.
  7. J. Yang, D. Zhang, and J.-Y. Yang, “Globally maximizing, locally minimizing: unsupervised discriminant projection with applications to face and palm biometrics,” IEEE Trans. Pattern Anal. Mach. Intell. 29, 650–664 (2007). [CrossRef]
  8. P. Belhumeur, J. Hesanha, and D. Kreigman, “Eigenfaces vs. Fisherfaces: recognition using class specific linear projection,” IEEE Trans. Pattern Anal. Mach. Intell. 19, 711–720 (1997). [CrossRef]
  9. Z. L. Zhang, F. Yang, W. Tan, J. Jia, and J. Yang, “Gabor feature based face recognition using supervised locality preserving projection,” Signal Process. 87, 2473–2483 (2007). [CrossRef]
  10. D. Cai, X. He, and K. Zhou, “Locality sensitive discriminant analysis,” in Proc. of International Joint Conf. on Artificial Intelligence, Hyderabad, India (2007), pp. 1–6.
  11. M. Sugiyama, “Local Fisher discriminant analysis for supervised dimensionality reduction,” in Proc. of the 23th International Conf. on Machine Learning, Pittsburgh, USA (2006), pp. 1–8.
  12. D. Cai, X. He, and J. Han, “Semi-supervised discriminant analysis,” in Tenth IEEE International Conf. on Computer Vision (IEEE, 2007), pp. 1–7.
  13. Y. Bengio, J. Palement, and P. Vincent, “Out-of-sample extensions for LLE, isomap, MOS, eigenmaps, and spectral clustering,” in Advances in Neural Information Processing Systems 6, Cambridge, MA (2003), p. 117.
  14. S. T. Roweis and L. K. Saul, “Nonlinear dimensionality reduction by locally linear embedding,” Science 290, 2323–2326 (2000). [CrossRef]
  15. M. Belkin and P. Niyogi, “Laplacian eigenmaps and spectral techniques for embedding and clustering,” in Advances in Neural Information Processing Systems 14, Vancouver, Canada (2001), pp. 585–591.
  16. M. H. Yang, N. Ahuja, and D. Kriegman, “Face recognition using kernel eigenfaces,” in Proceedings of International Conf. on Image Processing, Vancouver, Canada (2000), pp. 1–4.
  17. B. Scholkopf, A. Smola, and K. R. Muller, “Nonlinear component analysis as a kernel eigenvalues problem,” Neural Comput. 10, 1299–1319 (1998). [CrossRef]
  18. D. Hu, G. Feng, and Z. Zhou, “Two-dimensional locality preserving projections (2DLPP) with its application to palmprint recognition,” Pattern Recogn. 40, 339–342 (2007). [CrossRef]
  19. Y. Lu, C. Lu, M. Qi, and S. Wang, “A supervised locality preserving projections based local matching algorithm for face recognition,” Lect. Notes Comput. Sci. 6059, 28–37 (2010). [CrossRef]
  20. S. Zhang, “Semi-supervised locality preserving projections with compactness enhancement,” in 2010 International Conf. on Educational and Information Technology, Chongqing, China (2010), pp. 460–464.
  21. J. Cheng, Q. Liu, H. Lu, and Y.-W. Chen, “Supervised kernel locality preserving projections for face recognition,” Neurocomputing 67, 443–449 (2005). [CrossRef]
  22. D. Cai, X. He, and L. Han, “Orthogonal Laplacianfaces for face recognition,” IEEE Trans. Image Process. 15, 3608–3614 (2006). [CrossRef]
  23. J. Wright, A. Yang, A. Ganesh, S. Shastry, and Y. Ma, “Robust face recognition via sparse representation,” IEEE Trans. Pattern Anal. Mach. Intell. 31, 210–227 (2009). [CrossRef]
  24. E. J. Candès and M. B. Wakin, “An introduction to compressive sampling,” IEEE Signal Process. Mag. 25 (2), 21–30 (2008). [CrossRef]
  25. F. Xiang, Z. Wang, and X. Yuan, “Image reconstruction based on sparse and redundant representation model: local vs nonlocal,” Optik (2012). [CrossRef]
  26. Y. D. Leeuw and D. Cohen, “Diffusion in sparse networks: linear to semilinear crossover,” Phys. Rev. E 86, 051120 (2012). [CrossRef]
  27. Y. Sun, J. Zhao, and Y. Hu, “Supervised sparsity preserving projections for face recognition,” Proc. SPIE 8009, 80092D (2011). [CrossRef]
  28. F. Yin, L. C. Jiao, F. Shang, S. Wang, and B. Hou, “Fast Fisher sparsity preserving projections,” Neural Comput. Appl.1–15 (2012). [CrossRef]
  29. H. Wang, C. Yuan, W. Hub, and C. Sun, “Supervised class-specific dictionary learning or sparse modeling in action recognition,” Pattern Recogn. 45, 3902–3911 (2012). [CrossRef]
  30. N. Gu, M. Fan, H. Qiao, and B. Zhang, “Discriminative sparsity preserving projections for semi-supervised dimensionality reduction,” IEEE Signal Process. Lett. 19, 391–394 (2012). [CrossRef]
  31. L. Qiao, S. Chen, and X. Tan, “Sparsity preserving discriminant analysis for single training image face recognition,” Pattern Recogn. Lett. 31, 422–429 (2010). [CrossRef]
  32. R. P. W. Duin and E. Pekalska, “On refining dissimilarity matrices for an improved NN learning,” in Proceedings of 19th International Conf. on Pattern Recognition (IEEE, 2008), pp. 1–4.
  33. M. Raazia, D. G. Paul, and N. W. Joseph, “A matching pursuit based similarity measure for fuzzy clustering and classification of signals,” in Proceedings of IEEE International Conference on Fuzzy Systems, Hong Kong, China (2008), pp. 1950–1955.
  34. R. M. Willett, R. F. Marcia, and J. M. Nichols, “Compressed sensing for practical optical imaging systems: a tutorial,” Opt. Eng. 50, 072601 (2011). [CrossRef]
  35. J. Wright, Y. Ma, J. Mairal, G. Spairo, T. Huang, and S. Yan, “Sparse representation for computer vision and pattern recognition,” Proc. IEEE 98, 1031–1044 (2010). [CrossRef]
  36. M. Elad and M. Aharon, “Image denoising via sparse and redundant representations over learned dictionaries,” IEEE Trans. Image Process. 15, 3736–3745 (2006). [CrossRef]
  37. J. Ma and F.-X. L. Dimet, “Deblurring from highly incomplete measurements for remote sensing,” IEEE Trans. Geosci. Remote Sens. 47, 792–802 (2009). [CrossRef]
  38. L. Zhang, M. Yang, and X. Feng, “Sparse representation or collaborative representation: which helps face recognition?,” in Tenth IEEE International Conf. on Computer Vision (IEEE, 2011), pp. 1–8.
  39. G. Feng, D. Hu, and Z. Zhou, “A direct locality preserving projections (DLPP) algorithm for image recognition,” Neural Process. Lett. 27, 247–255 (2008).
  40. H. Yu and J. Yang, “A direct LDA algorithm for high-dimensional data with application to face recognition,” Pattern Recogn. 34, 2067–2070 (2001). [CrossRef]
  41. J. Yang, A. F. Frangi, J. Y. Yang, D. Zhang, and Z. Jin, “Kernel PCA plus LDA: a complete kernel Fisher discriminant framework for feature extraction and representation,” IEEE Trans. Pattern Anal. Mach. Intell. 27, 230–244 (2005). [CrossRef]
  42. S. A. Nene, S. K. Nayar, and H. Murase, “Columbia Object Image Library (COIL100),” Department of Computer Science, Columbia University Tech. Rep. No.  (1996).
  43. J. Wang, J. Yang, K. Yu, F. Lv, T. Huang, and Y. Gong, “Locality-constrained linear coding for image classification,” in 2010 IEEE Conf. on Computer Vision and Pattern Recognition (IEEE, 2010), pp. 3360–3367.

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