Bee Foraging Algorithm Based Multi-Level Thresholding For Image Segmentation

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. INDEX TERMS Bee foraging algorithm, image segmentation, meta-heuristic, multi-level thresholding.

Hybrid Optimization Algorithm for Multi-level Image Thresholding Using Salp Swarm Optimization Algorithm and Ant Colony Optimization

“…Standard parameters like mean, standard deviation, peak signal to noise ratio and structural similarity index are considered for analysis. For the given input the parameters like mean, standard deviation are measured and listed in Table I [27]. Additionally conventional ACO and SSO models are executed separately and their results are included in the comparative analysis.…”

Horizontal and vertical search artificial bee colony for image segmentation of COVID-19 X-ray images

“…Zhao et al [ 14 ] proposed an improved ant colony optimizer (RCACO) with a random spare strategy and chaotic intensification strategy to improve the processing efficiency of MTIS. Zhang et al [ 15 ] presented a novel population-based bee foraging algorithm (BFA) to enhance the search efficiency of MTIS. Xing et al [ 16 ] proposed a multi-threshold image segmentation method based on a thermal exchange optimization (TEO) algorithm, used to reduce the algorithm complexity of MTIS.…”

“… Zhao et al [ 14 ] RCACO + Kapur's entropy standard images This method improves the classic Kapur's entropy thresholding segmentation method's segmentation consistency and accuracy. Zhang et al [ 15 ] improved BFA + Otsu benchmark images This method effectively uses image processing, and the target area segmentation is more complete. Xing et al [ 16 ] TEO + gray-level co-occurrence matrix satellite images This method improves the accuracy and robustness of traditional image segmentation methods.…”

Performance Comparison of Most Recently Proposed Evolutionary, Swarm Intelligence, and Physics-Based Metaheuristic Algorithms for Retinal Vessel Segmentation

“…In [40], a multilevel thresholding method for color image segmentation exploiting 1D OTSU cuckoo search (CS), 1D OTSU lightning search (LSA), and cuttlefish (CFA) algorithms has been proposed by Bhandari and friends. e work presented in [41] describes a novel bee foraging algorithm (BFA) based multilevel thresholding method for image segmentation. In [42], the task of designing an efficient methodology based on Harris Hawks Optimization (HHO) algorithm for multilevel image segmentation has been investigated.…”