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

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Book cover Data Science and Analytics (REDSET 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 799))

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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.

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Author information

Authors and Affiliations

  1. Netaji Subhas Institute of Technology, New Delhi, 110078, India

    Swati Aggarwal, Lehar Bhandari, Karan Kapoor & Jaswin Kaur

Authors
  1. Swati Aggarwal
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  2. Lehar Bhandari
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  3. Karan Kapoor
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  4. Jaswin Kaur
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Corresponding author

Correspondence to Karan Kapoor .

Editor information

Editors and Affiliations

  1. University of Arkansas, Fayetteville, AR, USA

    Brajendra Panda

  2. GD Goenka University, Gurugram, Haryana, India

    Sudeep Sharma

  3. GD Goenka University, Gurugram, Haryana, India

    Nihar Ranjan Roy

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Aggarwal, S., Bhandari, L., Kapoor, K., Kaur, J. (2018). Weighted Fuzzy KNN Optimized by Simulated Annealing for Classification of Large Data: A New Approach to Skin Detection. In: Panda, B., Sharma, S., Roy, N. (eds) Data Science and Analytics. REDSET 2017. Communications in Computer and Information Science, vol 799. Springer, Singapore. https://doi.org/10.1007/978-981-10-8527-7_14

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  • DOI: https://doi.org/10.1007/978-981-10-8527-7_14

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-8526-0

  • Online ISBN: 978-981-10-8527-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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