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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
  • 1.1k Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 799)

Abstract

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.

Keywords

Classification Skin detection RGB color space Fuzzy KNN  SA TPR FPR 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Netaji Subhas Institute of TechnologyNew DelhiIndia

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