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A Performance Comparison of Supervised and Unsupervised Image Segmentation Methods

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Abstract

Image processing plays a vital role in many recent computer applications in the association with machine learning technology. The supervised training on dataset of features can only be successful if the segmentation process is accurate in the computer vision phase. The term segmentation is the process of extracting or identification of distinguishable regions in an image. This is performed based on the properties of image pixel intensity values and their proximities. This paper mainly focuses on an investigation of various latest image segmentation techniques performed in the field of computer vision and image processing. Segmentation plays a vital role in computer vision since any fault in segmentation will led to inaccurate extraction of features which results in wrong prediction of the decision support systems.

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Authors and Affiliations

  1. Karunya Institute of Technology and Sciences, Coimbatore, India

    Diana Baby

  2. Department of IT, Viswajyothi College of Engineering and Technology, Muvattupuzha, Kerala, India

    Diana Baby

  3. Department of CSE, Karunya Institute of Technology and Sciences, Coimbatore, India

    Sujitha Juliet Devaraj

  4. Department of CSE, Viswajyothi College of Engineering and Technology, Muvattupuzha, Kerala, India

    Soumya Mathew & M. M. Anishin Raj

  5. School of Computing, SASTRA Deemed University, Thanjavur, Tamil Nadu, India

    B. Karthikeyan

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  1. Diana Baby
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  2. Sujitha Juliet Devaraj
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  4. M. M. Anishin Raj
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Correspondence to Diana Baby.

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This article is part of the topical collection “Advances in Computational Approaches for Artificial Intelligence, Image Processing, IoT and Cloud Applications” guest edited by Bhanu Prakash K N and M.Shivakumar.

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Baby, D., Devaraj, S.J., Mathew, S. et al. A Performance Comparison of Supervised and Unsupervised Image Segmentation Methods. SN COMPUT. SCI. 1, 122 (2020). https://doi.org/10.1007/s42979-020-00136-9

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