An Artistic Image Segmentation Method Based on an Art-Design-Strategy-Improved Parrot Optimizer

Abstract

Multi-threshold image segmentation is an important research topic in the fields of computer vision and image processing. Its core objective is to efficiently determine the optimal threshold combination within a high-dimensional and complex search space. However, as the number of thresholds and image complexity increase, the computational cost of traditional exhaustive search methods grows exponentially. Meanwhile, conventional swarm intelligence algorithms often suffer from unstable convergence, premature stagnation, and parameter sensitivity when dealing with high-dimensional composite functions. To address these issues, this paper proposes an enhanced optimization algorithm termed the Parrot Optimizer with Artistic Design Strategy (PO-ADS). The proposed method constructs a multi-strategy cooperative optimization framework that integrates an Evolution Feedback–Based Adaptive Control Strategy (EFACS), a Multi-Operator Cooperative Evolution Strategy (MOCES), and an Artistic Design Strategy (ADS). These strategies enable dynamic parameter adjustment, adaptive balance between global exploration and local exploitation, and structured perturbation enhancement mechanisms. Experimental results on the CEC2020 and CEC2022 benchmark suites demonstrate that PO-ADS significantly outperforms seven state-of-the-art optimization algorithms across different dimensional settings in terms of optimization accuracy, convergence speed, and stability. The Friedman test results show that, on the CEC2020 benchmark suite, PO-ADS achieves average ranks of 1.72 (30-dimensional) and 1.85 (50-dimensional), both statistically superior to the comparative algorithms. Furthermore, PO-ADS is applied to multi-threshold image segmentation based on the Otsu criterion. The results indicate that the proposed method achieves optimal or near-optimal performance in terms of SSIM, PSNR, FSIM, and objective function values. Overall, the experimental findings confirm that PO-ADS not only possesses strong numerical optimization capability but also demonstrates robust and practical applicability in real-world image segmentation tasks.