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Multi-level Image Segmentation using Randomized Spiral-based Whale Optimization Algorithm

[ Vol. 15 , Issue. 5 ]


Basu Dev Shivahare* and S.K. Gupta   Pages 13 - 25 ( 13 )


Background: Segmenting an image into multiple regions is a pre-processing phase of computer vision. For the same, determining an optimal set of thresholds is a challenging problem.

Objective: This paper introduces a novel multi-level thresholding based image segmentation method.

Methods: The presented method uses a novel variant of whale optimization algorithm to determine the optimal thresholds. For experimental validation of the proposed variant, twenty-three benchmark functions are considered. To analyze the efficacy of new multi-level image segmentation method, images from Berkeley Segmentation Dataset and Benchmark (BSDS300) have been considered and tested against recent multi-level image segmentation methods.

Results: The segmentation results are validated in terms of subjective and objective evaluation.

Conclusion: Experiments arm that the presented method is efficient and competitive than the existing multi-level image segmentation methods.


Multi-level thresholding, whale optimization algorithm, image segmentation, swarm intelligence, optimization, berkeley.


Department of Computer Science & Engg., A.K.T.U. Lucknow, Uttar Pradesh, Department of Computer Science & Engg., B.I.E.T. Jhansi, Uttar Pradesh

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