Abstract
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.
Keywords: Multi-level thresholding, whale optimization algorithm, image segmentation, swarm intelligence, optimization, berkeley.
Graphical Abstract
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