A population-based automatic clustering algorithm for image segmentation
Seyed Jalaleddin Mousavirad, Gerald Schaefer, Mahshid Helali Moghadam, Mehrdad Saadatmand, Mahdi Pedram · Proceedings of the Genetic and Evolutionary Computation Conference Companion · 2021
Clustering is one of the prominent approaches for image segmentation. Conventional algorithms such as k-means, while extensively used for image segmentation, suffer from problems such as sensitivity to initialisation and getting stuck in local optima. To overcome these, population-based metaheuristic algorithms can be employed. This paper proposes a novel clustering algorithm for image segmentation based on the human mental search (HMS) algorithm, a powerful population-based algorithm to tackle optimisation problems. One of the advantages of our proposed algorithm is that it does not require any information about the number of clusters. To verify the effectiveness of our proposed algorithm, we present a set of experiments based on objective function evaluation and image segmentation criteria to show that our proposed algorithm outperforms existing approaches.