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

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 s...

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Autores principales: Mittal, Himanshu, Pandey, Avinash Chandra, Saraswat, Mukesh, Kumar, Sumit, Pal, Raju, Modwel, Garv
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7870780/
https://www.ncbi.nlm.nih.gov/pubmed/33584121
http://dx.doi.org/10.1007/s11042-021-10594-9
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author Mittal, Himanshu
Pandey, Avinash Chandra
Saraswat, Mukesh
Kumar, Sumit
Pal, Raju
Modwel, Garv
author_facet Mittal, Himanshu
Pandey, Avinash Chandra
Saraswat, Mukesh
Kumar, Sumit
Pal, Raju
Modwel, Garv
author_sort Mittal, Himanshu
collection PubMed
description 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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spelling pubmed-78707802021-02-09 A comprehensive survey of image segmentation: clustering methods, performance parameters, and benchmark datasets Mittal, Himanshu Pandey, Avinash Chandra Saraswat, Mukesh Kumar, Sumit Pal, Raju Modwel, Garv Multimed Tools Appl 1174: Futuristic Trends and Innovations in Multimedia Systems Using Big Data, IoT and Cloud Technologies (FTIMS) 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. Springer US 2021-02-09 2022 /pmc/articles/PMC7870780/ /pubmed/33584121 http://dx.doi.org/10.1007/s11042-021-10594-9 Text en © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle 1174: Futuristic Trends and Innovations in Multimedia Systems Using Big Data, IoT and Cloud Technologies (FTIMS)
Mittal, Himanshu
Pandey, Avinash Chandra
Saraswat, Mukesh
Kumar, Sumit
Pal, Raju
Modwel, Garv
A comprehensive survey of image segmentation: clustering methods, performance parameters, and benchmark datasets
title A comprehensive survey of image segmentation: clustering methods, performance parameters, and benchmark datasets
title_full A comprehensive survey of image segmentation: clustering methods, performance parameters, and benchmark datasets
title_fullStr A comprehensive survey of image segmentation: clustering methods, performance parameters, and benchmark datasets
title_full_unstemmed A comprehensive survey of image segmentation: clustering methods, performance parameters, and benchmark datasets
title_short A comprehensive survey of image segmentation: clustering methods, performance parameters, and benchmark datasets
title_sort comprehensive survey of image segmentation: clustering methods, performance parameters, and benchmark datasets
topic 1174: Futuristic Trends and Innovations in Multimedia Systems Using Big Data, IoT and Cloud Technologies (FTIMS)
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7870780/
https://www.ncbi.nlm.nih.gov/pubmed/33584121
http://dx.doi.org/10.1007/s11042-021-10594-9
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