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Nature-Inspired Optimization Algorithms and Their Application in Multi-Thresholding Image Segmentation

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Abstract

In the field of image processing, there are several problems where an efficient search of the solutions has to be performed within a complex search domain to find an optimal solution. Multi-thresholding which is a very important image segmentation technique is one of them. The multi-thresholding problem is simply an exponential combinatorial optimization process which traditionally is formulated based on complex objective function criterion which can be solved using only nondeterministic methods. Under such circumstances, there is also no unique measurement which quantitatively judges the quality of a given segmented image. Therefore, researchers are solving those issues by using Nature-Inspired Optimization Algorithms (NIOAs) as alternative methodologies for the multi-thresholding problem. This study presents an up-to-date review on all most important NIOAs employed in multi-thresholding based image segmentation domain. The key issues which are involved during the formulation of NIOAs based image multi-thresholding models are also discussed here.

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Authors and Affiliations

  1. Department of Computer Science and Application, Midnapore College (Autonomous), Paschim Medinipur, West Bengal, India

    Krishna Gopal Dhal

  2. Department of Information Technology, Kalyani Government Engineering College, Kalyani, Nadia, India

    Arunita Das

  3. Mass Software Solutions Pvt. Ltd., Kolkata, West Bengal, India

    Swarnajit Ray

  4. Departamento de Electrónica, Universidad de Guadalajara, CUCEI Av. Revolución 1500, 44430, Guadalajara, Mexico

    Jorge Gálvez

  5. Department of Engineering and Technological Studies, University of Kalyani, Kalyani, Nadia, India

    Sanjoy Das

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  1. Krishna Gopal Dhal
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Dhal, K.G., Das, A., Ray, S. et al. Nature-Inspired Optimization Algorithms and Their Application in Multi-Thresholding Image Segmentation. Arch Computat Methods Eng 27, 855–888 (2020). https://doi.org/10.1007/s11831-019-09334-y

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