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.