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...

Full description

Saved in:
Bibliographic Details
Published inMultimedia tools and applications Vol. 81; no. 24; pp. 35001 - 35026
Main Authors Mittal, Himanshu, Pandey, Avinash Chandra, Saraswat, Mukesh, Kumar, Sumit, Pal, Raju, Modwel, Garv
Format Journal Article
LanguageEnglish
Published New York Springer US 01.10.2022
Springer Nature B.V
Subjects
1174: Futuristic Trends and Innovations in Multimedia Systems Using Big Data
Abnormalities
Animals
Benchmarks
Clustering
Computer Communication Networks
Computer Science
Computer vision
Data Structures and Information Theory
Datasets
Heuristic methods
Histograms
Image retrieval
Image segmentation
IoT and Cloud Technologies (FTIMS)
Medical imaging
Multimedia
Multimedia Information Systems
Object recognition
Parameters
Special Purpose and Application-Based Systems
Image segmentation
Performance parameters
Benchmark datasets
Clustering methods
Online AccessGet full text

Cover

More Information
Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-021-10594-9