Weighted Fuzzy KNN Optimized by Simulated Annealing for Classification of Large Data: A New Approach to Skin Detection

  • Swati Aggarwal
  • Lehar Bhandari
  • Karan KapoorEmail author
  • Jaswin Kaur
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 799)


Machine learning is being used in every field. In almost all technical and financial domains, machine learning is being used enormously from predicting new outcomes to classifying a given data into multiple sets. In this research work it has been tried to build and expand upon previously built binary classifiers to develop a unique classifier for skin detection that separates the given input data into two sets – Skin segment and Non-Skin segment. Skin Detection essentially means detecting in an image or video pixels or regions which are of the skin color. The input data given to the classifier has three attributes - value of the red, green and blue channel. The combination of these three values is the color of the object seen. The classifier classifies the input data into the above two classes on the basis of these attributes. In general, this classifier can be extended to any binary class data.


Classification Skin detection RGB color space Fuzzy KNN  SA TPR FPR 


  1. 1.
    Alpaydin, E.: Introduction to Machine Learning (2010). [Sl]Google Scholar
  2. 2.
    Elgammal, A., Muang, C., Hu, D.: Skin detection-a short tutorial. Encycl. Biom., 1–10 (2009)Google Scholar
  3. 3.
    Alala, B., Mwangi, W., Okeyo, G.: Image representation using RGB color space. Int. J. Innov. Res. Dev. 3(8) (2014)Google Scholar
  4. 4.
    Keller, J.M., Gray, M.R., Givens, J.A.: A fuzzy k-nearest neighbor algorithm.IEEE Trans. Syst. Man Cybern. (4), 580–585 (1985)Google Scholar
  5. 5.
    Van Laarhoven, P.J., Aarts, E.H.: Simulated annealing. In: Simulated annealing:Theory and applications, pp. 7–15. Springer, Netherlands (1987)Google Scholar
  6. 6.
    Bhatt, R., Dhall, A.: Skin segmentation dataset. UCI Machine LearningRepository (2010)Google Scholar
  7. 7.
    Vezhnevets, V., Sazonov, V., Andreeva, A.: A survey on pixel-basedskin color detection techniques. In: Proceedings of the Graphicon, vol. 3, pp. 85–92, September 2003Google Scholar
  8. 8.
    Jones, M.J., Rehg, J.M.: Statistical color models with application to skin detection. Int. J. Comput. Vis. 46(1), 81–96 (2002)CrossRefGoogle Scholar
  9. 9.
    Kakumanu, P., Makrogiannis, S., Bourbakis, N.: A survey of skin-colormodeling and detection methods. Pattern Recognit. 40(3), 1106–1122 (2007)zbMATHGoogle Scholar
  10. 10.
    Brand, J., Mason, J.S.: A comparative assessment of three approaches topixel-level human skin-detection. In: Proceedings of the 15th International Conference on Pattern Recognition, vol. 1, pp. 1056–1059. IEEE (2000)Google Scholar
  11. 11.
    Jedynak, B., Zheng, H., Daoudi, M.: Maximum entropy models for skin detection. In: International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, pp. 180–193. Springer, Heidelberg, July 2003Google Scholar
  12. 12.
    Brown, D. A., Craw, I., Lewthwaite, J.: A SOM based approach to skin detection with application in real time systems. In: BMVC, vol. 1, pp. 491–500, July 2001Google Scholar
  13. 13.
    Lee, J.Y., Yoo, S.I.: An elliptical boundary model for skin color detection. In: Proceedings of the 2002 International Conference on Imaging Science, Systems, and Technology, June 2002Google Scholar
  14. 14.
    Joenes, M., Rehg, J.: Statistical color models with application to skin detection. In: IEEE Conference Computer Vision and Pattern Recognition. In: CVPR, vol. 99, pp. 274–280 (1999)Google Scholar
  15. 15.
    Sebe, N., Cohen, I., Huang, T.S., Gevers, T.: Skin detection: a Bayesian network approach. In: Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004, vol. 2, pp. 903–906. IEEE, August 2004Google Scholar
  16. 16.
    Setnes, M., Roubos, H.: GA-fuzzy modeling and classification: complexity and performance. IEEE Trans. Fuzzy Syst. 8(5), 509–522 (2000)CrossRefGoogle Scholar
  17. 17.
    Saha, P., Mandal, R.: Detection of Dengue Disease Using Artificial Neural Networks (2017)Google Scholar
  18. 18.
    Imandoust, S.B., Bolandraftar, M.: Application of k-nearest neighbor (knn) approach for predicting economic events: Theoretical background. Int. J. Eng. Res. Appl. 3(5), 605–610 (2013)Google Scholar
  19. 19.
    Goffe, W.L., Ferrier, G.D., Rogers, J.: Global optimization of statistical functions with simulated annealing. J. Econom. 60(1-2), 65–99 (1994)CrossRefGoogle Scholar
  20. 20.
    Liensberger, C., Stöttinger, J., Kampel, M.: Color-based skin detection and its application in video annotationGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Netaji Subhas Institute of TechnologyNew DelhiIndia

Personalised recommendations