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

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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.

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Authors and Affiliations

  1. Software Engineering Department, Altinbas University, Istanbul, Turkey

    Taymaz Rahkar Farshi

  2. Computer Engineering Department, University Collage of Nabi Akram, Tabriz, Iran

    Mohanna Orujpour

Authors
  1. Taymaz Rahkar Farshi
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  2. Mohanna Orujpour
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Correspondence to Taymaz Rahkar Farshi.

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Rahkar Farshi, T., Orujpour, M. Multi-level image thresholding based on social spider algorithm for global optimization. Int. j. inf. tecnol. 11, 713–718 (2019). https://doi.org/10.1007/s41870-019-00328-4

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  • DOI: https://doi.org/10.1007/s41870-019-00328-4

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