Spatially context aware multilevel color image segmentation using a Hybrid Artificial Hummingbird and Great Deluge mechanism

Abstract

Abstract Multilevel image thresholding is an important segmentation technique that partitions an image into meaningful regions in applications such as object recognition, medical imaging, and satellite image analysis. However, conventional techniques are limited by their poor sensitivity to initial conditions, lack of sufficient spatial contextual information, and slow convergence. The proposed method uses a novel hybrid model based on the Artificial Hummingbird Algorithm (AHA) to address the limitations of existing approaches. Here, Latin Hypercube, Sobol, Halton, and Sierpinski strategies are used during the population initialization phase to improve population diversity and search space coverage. This improves the exploration capability of the algorithm and supports better search space. The proposed methodology also uses spatial contextual information to improve the quality of segmentation. It also incorporates a relationship between neighboring pixels in order to retain more structure and enhance the visual performance of the output. For the exploitation phase, the Great Deluge Algorithm (GDA) is utilized as the optimization algorithm. Furthermore, the use of GDA serves as an adaptive acceptance function which reduces the chance of getting stuck during the search process. Minimum Cross Entropy Measure (MCEM) is used as the objective function to obtain optimal threshold values. Different evaluation metrics have been used to compare the results of the proposed method with other existing metaheuristic algorithms. The code is available at https://github.com/suprajatirumalasetti/AHA_GDA_Image_Segmentation_Code .