Classification via ensembles of basic thresholding classifiers

IET Computer Vision

Volumn 10, Issue 5, 2016, Pages 433-442

  Toksöz, Mehmet Altan ^a   Ulusoy, İlkay ^a  

^a MIDDLE EAST TECHNICAL UNIVERSITY   (Turkey)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION ACCURACY; CLASSIFICATION ALGORITHM; CLASSIFICATION RESULTS; CONDITION; FACE IDENTIFICATION; NUMERICAL RESULTS; RANGE SPACES; TEST SAMPLES; THRESHOLD PARAMETERS; THRESHOLDING;

EID: 85012998736     PISSN: 17519632     EISSN: 17519640     Source Type: Journal    
DOI: 10.1049/IET-CVI.2015.0077     Document Type: Article

References (30)

1. Turk, M., Pentland, A.: ‘Eigenfaces for recognition’, J. Cogn. Neurosci., 1991, 3, (1), pp. 71–86
2. Belhumeur, P.N., Hespanha, J.P., Kriegman, D.: ‘Eigenfaces vs. fisherfaces: recognition using class specific linear projection’, IEEE Trans. Pattern Anal. Mach. Intell., 1997, 19, (7), pp. 711–720
3. Huang, K., Aviyente, S.: ‘Sparse representation for signal classification’. Advances in Neural Information Processing Systems, 2006, pp. 609–616
4. Wright, J., Yang, A.Y., Ganesh, A., et al.: ‘Robust face recognition via sparse representation’, IEEE Trans. Pattern Anal. Mach. Intell., 2009, 31, (2), pp. 210–227
5. Yang, A.Y., Sastry, S.S., Ganesh, A., et al.: ‘Fast l1-minimization algorithms and an application in robust face recognition: a review’. 17th IEEE Int. Conf. on Image Processing (ICIP), 2010, pp. 1849–1852
6. Osborne, M.R., Presnell, B., Turlach, B.A.: ‘A new approach to variable selection in least squares problems’, IMA J. Numer. Anal., 2000, 20, (3), pp. 389–403
7. Fawzi, A., Davies, M., Frossard, P.: ‘Dictionary learning for fast classification based on soft thresholding’, Int. J. Comput. Vis., 2015, 114, (2), pp. 306–321
8. Shi, Q., Li, H., Shen, C.: ‘Rapid face recognition using hashing’. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2010, pp. 2753–2760
9. Chen, Y., Nasrabadi, N.M., Tran, T.D.: ‘Hyperspectral image classification using dictionary-based sparse representation’, IEEE Trans. Geosci. Remote Sens., 2011, 49, (10), pp. 3973–3985
10. Chen, Y., Nasrabadi, N., Tran, T.: ‘Hyperspectral image classification via kernel sparse representation’, IEEE Trans. Geosci. Remote Sens., 2013, 51, (1), pp. 217–231
11. Zhang, H., Li, J., Huang, Y., et al.: ‘A nonlocal weighted joint sparse representation classification method for hyperspectral imagery’, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 2014, 7, (6), pp. 2056–2065
12. Zou, J., Li, W., Huang, X., et al.: ‘Classification of hyperspectral urban data using adaptive simultaneous orthogonal matching pursuit’, J. Appl. Remote Sens., 2014, 8, (1), pp. 085 099:1–085 099:14
13. Tropp, J.A., Gilbert, A.C.: ‘Signal recovery from random measurements via orthogonal matching pursuit’, IEEE Trans. Inf. Theory, 2007, 53, (12), pp. 4655–4666
14. Shi, Q., Eriksson, A., Van Den Hengel, A., et al.: ‘Is face recognition really a compressive sensing problem?’. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2011, pp. 553–560
15. Rauhut, H., Schnass, K., Vandergheynst, P.: ‘Compressed sensing and redundant dictionaries’, IEEE Trans. Inf. Theory, 2008, 54, (5), pp. 2210– 2219
16. Foucart, S., Rauhut, H.: ‘A mathematical introduction to compressive sensing’ (Springer, 2013)
17. Johnson, W.B., Lindenstrauss, J.: ‘Extensions of Lipschitz mappings into a Hilbert space’, Contemp. Math., 1984, 26, (1), pp. 189–206
18. Mairal, J., Bach, F., Ponce, J., et al.: ‘Online learning for matrix factorization and sparse coding’, J. Mach. Learn. Res., 2010, 11, pp. 19–60
19. Golub, G.H., Hansen, P.C., O'Leary, D.P.: ‘Tikhonov regularization and total least squares’, SIAM J. Matrix Anal. Appl., 1999, 21, (1), pp. 185–194
20. Tropp, J.A.: ‘Greed is good: algorithmic results for sparse approximation’, IEEE Trans. Inf. Theory, 2004, 50, (10), pp. 2231–2242
21. Eldar, Y.C., Kuppinger, P., Bolcskei, H.: ‘Block-sparse signals: uncertainty relations and efficient recovery’, IEEE Trans. Signal Process., 2010, 58, (6), pp. 3042–3054
22. Woźniak, M., Graña, M., Corchado, E.: ‘A survey of multiple classifier systems as hybrid systems’, Inf. Fusion, 2014, 16, pp. 3–17
23. Krawczyk, B., Woźniak, M., Cyganek, B.: ‘Clustering-based ensembles for one-class classification’, Inf. Sci., 2014, 264, pp. 182–195
24. Krawczyk, B., Woźniak, M., Herrera, F.: ‘On the usefulness of one-class classifier ensembles for decomposition of multi-class problems’, Pattern Recognit., 2015, 48, (12), pp. 3969–3982
25. Cyganek, B.: ‘One-class support vector ensembles for image segmentation and classification’, J. Math. Imaging Vis., 2012, 42, (2-3), pp. 103–117
26. Yang, A.Y., Wright, J., Ma, Y., et al.: ‘Feature selection in face recognition: a sparse representation perspective’. Technical Report, UC Berkeley Tech Report UCB/EECS-2007-99, 2007
27. Galar, M., Fernández, A., Barrenechea, E., et al.: ‘Drcw-ovo: distance-based relative competence weighting combination for one-vs-one strategy in multi-class problems’, Pattern Recognit., 2015, 48, (1), pp. 28–42
28. Cohen, J.: ‘A coefficient of agreement for nominal scales’, Educ. Psychol. Meas., 1960, 20, (1), pp. 37–46
29. Viola, P., Jones, M.: ‘Rapid object detection using a boosted cascade of simple features’. Proc. of the 2001 IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, 2001, vol. 1, pp. 511–518
30. Spacek, L.: ‘Collection of facial images’. Available at http://cswww.essex.ac.uk/mv/allfaces/index.html, accessed September 2007