Abstract
Accurate image segmentation is the preprocessing step of image processing. Multi-level threshold segmentation has important research value in image segmentation, which can effectively solve the problem of region analysis of complex images, but the computational complexity increases accordingly. In order to overcome this problem, an modified Dragonfly algorithm (MDA) is proposed to determine the optimal combination of different levels of thresholds for color images. Chaotic mapping and elite opposition-based learning strategies (EOBL) are used to improve the randomness of the initial population. The hybrid algorithm of Dragonfly Algorithms (DA) and Differential Evolution (DE) is used to balance the two basic stages of optimization: exploration and development. Kapur entropy, minimum cross-entropy and Otsu method are used as fitness functions of image segmentation. The performance of 10 test color images is evaluated and compared with 9 different meta-heuristic algorithms. The results show that the color image segmentation method based on MDA is more effective and accurate than other competitors in average fitness value (AF), standard deviation (STD), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM) and feature similarity index (FSIM). Friedman test and Wilcoxon's rank sum test are also performed to assess the significant difference between the algorithms.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
