Abstract
Multilevel thresholding is a very important image processing technique in the field of image segmentation. However, the computational complexity of determining the optimal threshold grows exponentially with increasing thresholds. To overcome this drawback, in this paper, we propose a multi-threshold image segmentation method based on the moth swarm algorithm. The meta-heuristic algorithm uses Kapur’s entropy method to optimize the thresholds for eight standard test images. When compared with other state-of-the-art evolutionary algorithms, the proposed method proved to be robust and effective according to numerical experimental results and image segmentation results. This indicates the high performance of the method for the segmentation of digital images.
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