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International Journal of Information Technology

, Volume 11, Issue 4, pp 713–718 | Cite as

Multi-level image thresholding based on social spider algorithm for global optimization

  • Taymaz Rahkar FarshiEmail author
  • Mohanna Orujpour
Original Research

Abstract

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.

Keywords

Image segmentation Multilevel thresholding Social spider algorithm Otsu’s function 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no competing interests.

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Copyright information

© Bharati Vidyapeeth's Institute of Computer Applications and Management 2019

Authors and Affiliations

  1. 1.Software Engineering DepartmentAltinbas UniversityIstanbulTurkey
  2. 2.Computer Engineering DepartmentUniversity Collage of Nabi AkramTabrizIran

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