A Performance Comparison of Supervised and Unsupervised Image Segmentation Methods

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 proce...

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Bibliographic Details
Published in:SN computer science 2020-05, Vol.1 (3), p.122, Article 122
Main Authors: Baby, Diana, Devaraj, Sujitha Juliet, Mathew, Soumya, Anishin Raj, M. M., Karthikeyan, B.
Format: Article
Language:English
Subjects:
Advances in Computational Approaches for Artificial Intelligence
Algorithms
Clustering
Computer Imaging
Computer Science
Computer Systems Organization and Communication Networks
Computer vision
Data Structures and Information Theory
Decision support systems
Dictionaries
Image Processing
Image segmentation
Information Systems and Communication Service
IoT and Cloud Applications
Machine learning
Methods
Pattern Recognition and Graphics
Software Engineering/Programming and Operating Systems
Survey Article
Vision
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Summary: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.
ISSN:2662-995X
2661-8907
DOI:10.1007/s42979-020-00136-9