Fast Image Segmentation with Multilevel Threshold of Two-dimensional Entropy Based on ISSA and Integral Graph

In order to improve the performance and efficiency of image segmentation with multilevel threshold of two-dimensional entropy for practical industrial applications, this paper proposes a fast image segmentation method with multilevel threshold of two-dimensional entropy based on ISSA and integral graph. Firstly, we introduce and analyze the sparrow search algorithm (SSA) . To address the shortcomings of SSA, such as poor global search ability and easy to fall into local optimal solution, we propose an improved sparrow search algorithm (ISSA) based on Gaussian perturbation strategy with linear decreasing variance and moving strategy with random step size. Then, we further introduce the integral graph method to reduce the calculation amount of the entropy, use the entropy as the fitness function of ISSA to search the optimal threshold, and propose a fast algorithm for image segmentation with multilevel threshold of two-dimensional entropy based on ISSA and integral graph. Finally, we compare the proposed method with the existing segmentation algorithms, and the experimental results show that the proposed method improves the segmentation efficiency of image segmentation with multilevel threshold of two-dimensional entropy in industrial application scenarios.