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

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.