Abstract
Multi-level thresholding is one of the essential approaches for image segmentation. Determining the optimal thresholds for multi-level thresholding needs exhaustive searching which is time-consuming. To improve the searching efficiency, a novel population based bee foraging algorithm (BFA) for multi-level thresholding is presented in this paper. The proposed algorithm provides different flying trajectories for different types of bees and takes both single-dimensional and multi-dimensional search aiming to maintain a proper balance between exploitation and exploration. The bee swarm is divided into a number of sub-swarms to enhance the diversity. A neighbourhood shrinking strategy is applied to mitigate stagnation and accelerate convergence. Experiments have been performed on eight benchmark images using between-class variance as the thresholding criterion. The performance of the proposed algorithm is compared with some state-of-art meta-heuristic algorithms. The results show that BFA is efficient and robust, produces excellent results with few control parameters, and outperforms other algorithms investigated in this consideration on most of the tested images.
Highlights
Image segmentation is an essential technique for image processing, which is aiming to partition an image into a number of congeneric regions with similar characteristics using some pre-defined measurement criterions
The threshold results obtained by bee foraging algorithm (BFA) are compared with the results using some state-of-art swarm intelligent optimization algorithms including artificial bee colony (ABC), moth-flame optimization (MFO), grey wolf optimizer (GWO), and whale optimization algorithm (WOA)
In this work, a novel bee foraging algorithm (BFA) based multi-level thresholding method is presented for image segmentation
Summary
Image segmentation is an essential technique for image processing, which is aiming to partition an image into a number of congeneric regions with similar characteristics using some pre-defined measurement criterions. A novel optimization algorithm based on the intelligent foraging behaviour of bee swarm is proposed and applied to improve the thresholding-based image segmentation. The forager bees and onlooker bees constitute the recruit bees group to do the local search They apply different types of updating strategies to search the neighbourhood of each selected food source thoroughly. The recruit bees group contains two types of bees: foragers and onlookers They apply different strategies to select the food sources with higher quality, and use diverse tactics to update their positions in the searching space. That means foragers and onlookers occupy 45% of the total population respectively This kind of population allocation is inspired by some biological research and hypothesis of bee swarms in nature [30], which helps to reduce the number of control parameters for the algorithm. The optimum PSNR (dB) and SSIM values obtained by the tested algorithms
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