A Review on Applications of Metaheuristic Algorithms in Multilevel Thresholding Image Segmentation
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In the field of image analysis, segmentation is one of the most important pre-processing steps. One way to achieve segmentation is the use of threshold selection. In particular, multilevel image thresholding is a very important image processing technique that is used as a basis for image segmentation and further higher level processing. However, the required computational time for exhaustive search grows exponentially with the number of desired thresholds. Swarm intelligence metaheuristics are well known as successful and efficient optimization methods for intractable problems. With the focus on multilevel thresholding, a significant amount of research was globally carried out to explore the best optimal thresholds for segmenting the different application of images. In this article, a review has been reported on the applications of metaheuristic algorithms in multilevel thresholding of image segmentation problems on various output performance measures.
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