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Multilevel thresholding segmentation of color images plays an important role in many fields. The pivotal procedure of this technique is determining the specific threshold of the images. In this paper, an improved mayfly algorithm (IMA)-based color image segmentation method is proposed. Tent mapping initializes the female mayfly population to increase population diversity. Lévy flight is introduced in the wedding dance iterative formulation to make IMA jump from the local optimal solution quickly. Two nonlinear coefficients were designed to speed up the convergence of the algorithm. To better verify the effectiveness, eight benchmark functions are used to test the performance of IMA. The average fitness value, standard deviation, and Wilcoxon rank sum test are used as evaluation metrics. The results show that IMA outperforms the comparison algorithm in terms of search accuracy. Furthermore, Kapur entropy is used as the fitness function of IMA to determine the segmentation threshold. 10 Berkeley images are segmented. The best fitness value, peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and other indexes are used to evaluate the effect of segmented images. The results show that the IMA segmentation method improves the segmentation accuracy of color images and obtains higher quality segmented images. [ABSTRACT FROM AUTHOR]

The fuzzy c-means (FCM) algorithm is a popular method for data clustering and image segmentation. However, the main problem of this algorithm is that it is very sensitive to the initialization of primary clusters, so it may not perform well in segmenting complex images. Another problem with the FCM is the equal importance of the image features used during the segmentation process, which causes unstable performance on different images. In this paper, we propose an FCM-based color image segmentation approach, termed CGFFCM, applying an automatic cluster weighting scheme to reduce the sensitivity to the initialization, and a group-local feature weighting strategy to better image segmentation. Also, we combine the proposed clustering algorithm with the Imperialist Competitive Algorithm (ICA) to optimize the feature weighting process. In addition, we apply an efficient combination of image features to increase the segmentation quality. The performance of CGFFCM is evaluated and compared with state-of-the-art methods (such as SMKIFC (semi-supervised surrogate-assisted multi-objective kernel intuitionistic fuzzy clustering), and A-PSO-IT2IFCM (alternate particle swarm optimization based adaptive interval type-2 intuitionistic FCM clustering algorithm)) using the Berkeley benchmark dataset. The results obtained by CGFFCM are 95%, 79%, and 91%, in terms of average Accuracy , NMI , and F-score metrics, respectively, which all are better than the competitors. The implementation source code of CGFFCM is made publicly available at https://github.com/Amin-Golzari-Oskouei/CGFFCM. • A cluster weighting scheme to reduce the sensitivity of the image segmentation to the selection of initial centers. • A group-local feature weighting strategy to measure the importance and weights of image features precisely. • An automatic way to calculate the cluster and feature weights simultaneously during the learning process. • A combination of ICA meta-heuristic algorithm with proposed clustering algorithm to find the optimal group feature weights. [ABSTRACT FROM AUTHOR]