Multi-level image thresholding based on social spider algorithm for global optimization
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Tarih
2019
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer Science+Business Media B.V.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Thresholding is one of the simplest and popular technique for segmenting images. Maximum between-class variance (Otsu’s) method is one of the well-known and widely used method in case of segmentation. Not only Otsu could be used for bi-level thresholding but also it could be extended to multi-level image thresholding. Finding the optimum threshold values in multi-level case is very time consuming process, thus optimization algorithm can deal with this problem. In this paper social spider algorithm for global optimization has been used for maximizing the between-class variance to carry out multi-level image thresholding. Experimental outcomes have demonstrated that the proposed method is capable of estimating threshold values and yield satisfying outcome. © 2019, Bharati Vidyapeeth's Institute of Computer Applications and Management.
Açıklama
Anahtar Kelimeler
Image Segmentation, Multilevel Thresholding, Otsu’s Function, Social Spider Algorithm
Kaynak
International Journal of Information Technology (Singapore)
WoS Q Değeri
Scopus Q Değeri
Q1
Cilt
11
Sayı
4
