Construction of image segmentation system combining TC and swarm intelligence algorithm

Considering the slow convergence speed and local optima in most current swarm intelligence algorithms for image segmentation, this study combines the thresholding clustering with intelligent algorithms. The algorithm is optimized based on the characteristics of image segmentation. First, an improved differential evolution algorithm is applied to enhance the speed of threshold image segmentation. The convergence performance of the algorithm is improved by adjusting the crossover probability and mutation strategy. Second, considering the threshold difference, an improved two-dimensional Otsu threshold segmentation algorithm based on cuckoo algorithm is proposed, and fractional calculus processing and fractional enhancement filtering are introduced to improve the image segmentation quality. The loss value in image feature segmentation was less than 5 × 10 2 , the accuracy exceeded 90% in different types of image segmentation, the running time did not exceed 2 s, and the structural similarity reached 0.912, which was significantly better than other algorithms. Moreover, the pixel accuracy of the image processing algorithm exceeded 90%, with good segmentation details and edge extraction effects. The proposed image segmentation method performs well in segmentation accuracy, edge clarity, and computational efficiency, which can improve the segmentation accuracy and application effect in image processing systems and provide a method for improving the generalization ability of segmentation models in different scenarios.