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A comprehensive survey of image segmentation: clustering methods, performance parameters, and benchmark datasets

Authors
  • Mittal, Himanshu1
  • Pandey, Avinash Chandra1
  • Saraswat, Mukesh1
  • Kumar, Sumit2
  • Pal, Raju1
  • Modwel, Garv3
  • 1 Jaypee Institute of Information Technology,
  • 2 Amity University,
  • 3 Valeo India Private Limited, Chennai, Tamil Nadu India
Type
Published Article
Journal
Multimedia Tools and Applications
Publisher
Springer-Verlag
Publication Date
Feb 09, 2021
Pages
1–26
Identifiers
DOI: 10.1007/s11042-021-10594-9
PMID: 33584121
PMCID: PMC7870780
Source
PubMed Central
Keywords
License
Unknown

Abstract

Image segmentation is an essential phase of computer vision in which useful information is extracted from an image that can range from finding objects while moving across a room to detect abnormalities in a medical image. As image pixels are generally unlabelled, the commonly used approach for the same is clustering. This paper reviews various existing clustering based image segmentation methods. Two main clustering methods have been surveyed, namely hierarchical and partitional based clustering methods. As partitional clustering is computationally better, further study is done in the perspective of methods belonging to this class. Further, literature bifurcates the partitional based clustering methods into three categories, namely K-means based methods, histogram-based methods, and meta-heuristic based methods. The survey of various performance parameters for the quantitative evaluation of segmentation results is also included. Further, the publicly available benchmark datasets for image-segmentation are briefed.

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