Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/787
Title: D-MOSG: Discrete multi-objective shuffled gray wolf optimizer for multi-level image thresholding
Authors: Karakoyun, Murat
Gülcü, Şaban
Kodaz, Halife
Keywords: Multi-Level Thresholding
Image Segmentation
Multi-Objective Optimization
Shuffled Frog Leaping
Gray Wolf Optimizer
Segmentation
Algorithm
Entropy
Kapurs
Issue Date: 2021
Publisher: ELSEVIER - DIVISION REED ELSEVIER INDIA PVT LTD
Abstract: Segmentation is an important step of image processing that directly affects its success. Among the methods used for image segmentation, histogram-based thresholding is a very popular approach. To apply the thresholding approach, many methods such as Otsu, Kapur, Renyi etc. have been proposed in order to produce the thresholds that will segment the image optimally. These suggested methods usually have their own characteristics and are successful for particular images. It can be thought that better results may be obtained by using objective functions with different characteristics together. In this study, the thresholding which is originally applied as a single-objective problem has been considered as a multi-objective problem by using the Otsu and Kapur methods. Therefore, the discrete multi-objective shuffled gray wolf optimizer (D-MOSG) algorithm has been proposed for multi-level thresholding segmentation. Experiments have clearly shown that the D-MOSG algorithm has achieved superior results than the compared algorithms. (C) 2021 Karabuk University. Publishing services by Elsevier B.V.
URI: https://doi.org/10.1016/j.jestch.2021.03.011
https://hdl.handle.net/20.500.13091/787
ISSN: 2215-0986
Appears in Collections:Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections

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