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Research Article Peer ReviewedAt least two reviewers Open Access

A Performance Analysis of Extreme Learning Machine on Skin Segmentation Dataset

1Bursa Uludag University  
✉ Corresponding author: Ayşenur  Uzun   · aysenuruzun@uludag.edu.tr
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References

  1. D. Chai and K. N. Ngan, "Face segmentation using skin color map in videophone applications," IEEE Trans. CSVr, vol. 9.4, pp. 551-564, Jun. 1999.
  2. R.L. Hsu, M. Abdel-Mottaleb, and A. K. Jain, "Face detection in color images," IEEE Trans. PAMI, vol. 24.5, pp.696-796, May 2002.
  3. A.O. Bălan, and M.J. Black, "The naked truth: Estimating body shape under clothing," in European Conference on Computer Vision. Springer, Berlin, Heidelberg, 2008, pp. 15-29.
  4. X.Z.J.Y.A. Waibel, "Segmenting hands of arbitrary color," in IEEE the Fourth IEEE International Conference on Automatic Face and Gesture Recognition (FG'000), 2000, vol. 446, p.453.
  5. S.L Phung, A. Bouzerdoum, and D. Chai. "Skin segmentation using color and edge information," in Seventh International Symposium on Signal Processing and Its Applications, Proceedings, 2003, vol. 1, pp. 525-528
  6. L. Sigal, S. Sclaroff, and V. Athitsos. "Estimation and prediction of evolving color distributions for skin segmentation under varying illumination," in Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No. PR00662), 2000, vol. 2, pp. 152-159.
  7. J.P.B. Casati, D.R. Moraes, and E.L.L. Rodrigues, "SFA: A human skin image database based on FERET and AR facial images," in IX workshop de Visao Computational, Rio de Janeiro, 2013.
  8. A. Uçar, "Color face recognition based on steerable pyramid transform and extreme learning machines," The Scientific World Journal 2014, 2014.
  9. H.K. Al-Mohair, J.M. Saleh, and S.A. Suandi, "Hybrid human skin detection using neural network and k-means clustering technique," Applied Soft Computing, vol. 33, pp. 337-347, 2015.
  10. S.M. Jaisakthi, and S. Mohanavalli, "Skin Segmentation using Ensemble Technique," Research Journal of Applied Sciences, Engineering and Technology, vol. 9.11, pp. 963-968, 2015.
  11. Y. Lei, W. Yuan, H. Wang, Y. Wenhu and W.Bo, "A skin segmentation algorithm based on stacked autoencoders," IEEE Transactions on Multimedia, vol. 19.4, pp. 740-749, 2017.
  12. S. Kolkur, D. Kalbande, P. Shimpi, C. Bapat and J. Jatakia, "Human skin detection using RGB, HSV and YCbCr color models," arXiv preprint arXiv:1708.02694, vol. 137, pp. 324-332, 2017.
  13. T. Dastane, V. Rao, K. Shenoy and D. Vyavaharkar, "An Effective Pixel-Wise Approach for Skin Colour Segmentation-Using Pixel Neighbourhood Technique," International Journal on Recent and Innovation Trends in Computing and Communication, vol. 6.3, pp. 182-186, 2018.
  14. E.C. Kiziltas, A. Uzun, and E. Yılmaz, "Skin Segmentation by Using Complex Valued Neural Network with HSV Color Spaces," International Journal of Multidisciplinary Studies and Innovative Technologies, vol. 3.1, pp. 1-4, 2019.
  15. G.B. Huang, Q.Y. Zhu and C.K. Siew, “Extreme learning machine: theory and applications,” Neurocomputing, vol. 70.1-3, pp. 489-501, 2006.
  16. G.B. Huang H. Zhou, X. Ding, and R. Zhang, “Extreme learning machine for regression and multiclass classification,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 42.2, pp. 513-529, 2012.
  17. G.B. Huang, “An insight into extreme learning machines: random neurons, random features and kernels,” Cognitive Computation, vol.6.3, pp. 376-390, 2014.
  18. G. Huang, G.B. Huang, S. Song, and K. You, “Trends in extreme learning machines: A review,” Neural Networks, vol. 61, pp. 32-48, 2015.
  19. X. Deng, Q. Liu, Y. Deng and S. Mahadevan, "An improved method to construct basic probability assignment based on the confusion matrix for classification problem," Information Sciences, vol. 340, pp. 250-261, 2016.
  20. Yılmaz, E., “An expert system based on Fisher score and LS-SVM for cardiac arrhythmia diagnosis.” Computational and mathematical methods in medicine, 2013.
  21. P. Refailzadeh, L. Tang and H. Liu, Cross-validation, Encyclopedia of data base systems New York: Springer, pp. 532–538, 2009
  22. E. Yılmaz, “Fetal State Assessment from Cardiotocogram Data Using Artificial Neural Networks,” Journal of Medical and Biological Engineering, vol. 36.6, pp. 820-832, 2016.
Research Contributions (2019+)Peer reviewed by at least two reviewers
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