A multilevel thresholding algorithm using HDAFA for image segmentation

Segmentation of image is a key step in image analysis and pre-processing. It consists of separating the pixels into different segments based on their intensity level according to threshold values. The most challenging job in segmentation is to select the optimum threshold values. Standard multilevel thresholding (MT) techniques are effective for bi-level thresholds due to their simplicity, robustness, decreased convergence time and precision. As the level of thresholds increases, computational complexity also increases exponentially. To mitigate these issues various metaheuristic algorithm are applied to this problem. In this manuscript, a new hybrid version of the Dragonfly algorithm (DA) and Firefly Algorithm (FA) is proposed. DA is an optimization algorithm recently suggested based on the dragonfly's static and dynamic swarming behavior. DA's worldwide search capability is great with randomization and static swarm behavior, local search capability is restricted, resulting in local optima trapping alternatives. The firefly algorithm (FA) is influenced by fireflies' social behavior in which they generate flashlights to attract their mates. The suggested technique combines the ability to explore DA and firefly Algorithm’s ability to exploit to obtain ideal global solutions. In this paper, HDAFA is applied on ten standard test images having a diverse histogram, which are taken from Berkeley Segmentation Data Set 500 (BSDS500) benchmark image set for segmentation. The search capability of the algorithm is employed with OTSU and Kapur’s entropy MT as an objective functions for image segmentation. The proposed approach is compared with the existing state-of-art optimization algorithms like MTEMO, GA, PSO, and BF for both OTSU and Kapur’s entropy methods. Qualitative experimental outcomes demonstrate that HDAFA is highly efficient in terms of performance metric such as PSNR, mean, threshold values, number of iterations taken to converge and image segmentation quality.