基于改进沙猫群优化算法的Otsu图像分割

Enhanced Sand Cat Swarm Optimization for Otsu Image Segmentation

摘要: 图像分割作为计算机视觉领域的关键预处理步骤，其效果直接影响后续分析与理解。Otsu算法因其原理清晰、计算高效而在全局阈值分割中被广泛应用，但其存在计算复杂度较大，分割精度较低等问题，单一全局阈值往往难以获得理想的分割效果。为克服传统Otsu算法的局限并提升其性能，本文提出了一种基于改进沙猫群优化算法(SCSO-DOGA)的Otsu图像分割算法。针对原始沙猫群优化算法(Sand Cat Swarm Optimization, SCSO)在解决高维优化问题时可能存在的收敛速度慢、易陷入局部最优等不足，本文设计了改进策略，包括加入动态反向学习策略以及与雁群算法相融合，提升了算法性能和收敛效率。改进后的Otsu算法与其他图像分割算法作实验对比，选取ACC、Jaccard等作为评估指标，验证算法的性能。实验结果表明基于改进沙猫群优化算法的Otsu图像分割法能够更加准确地解决图像分割问题。

Abstract: Image segmentation serves as a critical preprocessing step in computer vision, directly impacting subsequent analysis and interpretation. While Otsu’s method is widely adopted for global threshold segmentation due to its conceptual clarity and computational efficiency, it suffers from high computational complexity and limited segmentation accuracy, often failing to achieve satisfactory results with a single global threshold. To overcome these limitations and enhance performance, this paper proposes an Otsu image segmentation algorithm based on an improved Sand Cat Swarm Optimization algorithm (SCSO-DOGA). Addressing the slow convergence and susceptibility to local optima observed in the original Sand Cat Swarm Optimization (SCSO) algorithm for high-dimensional optimization problems, this study introduces two key enhancements: a dynamic opposition-based learning strategy and hybridization with the Duck Optimization Algorithm. These improvements significantly boost algorithmic performance and convergence efficiency. The proposed Otsu algorithm is experimentally compared against other segmentation methods using evaluation metrics such as Accuracy and Jaccard index. Results demonstrate that the Otsu method based on the improved SCSO-DOGA algorithm resolves image segmentation problems with superior accuracy.