The image segmentation method based on clustering analysis has the advantages of small sample space constraints and strong universality. As an unsupervised clustering algorithm, the fuzzy C-means clustering algorithm is widely used in practical engineering. However, it is still some shortcomings: the fuzzy C-means clustering algorithm is difficult to interpret the noise effectively, which makes it more sensitive to the noise, and the selection of key parameters has to be made by trial and error experiments, reducing the adaptability of the algorithm. Besides, its iteration process is heavily influenced by the initial clustering centers and easy to fall into local optimum. Therefore, an intuitionistic Fuzzy C-means clustering method, based on local-information weight, is proposed in this paper. By introducing the local-information weight, the proposed algorithm adjusts the local-information influence weight adaptively in fuzzy partition, which enhances its robustness to noisy images. Furthermore, a novel swarm intelligence algorithm, called the Gold-Panning Algorithm, is proposed to optimize the initial clustering centers and key parameters in the clustering algorithm. By utilizing the Gold-Panning Algorithm, the adaptability of the proposed clustering algorithm is further improved. In this paper, the proposed methods are explained in detail and compared with the existing methods to demonstrate its superior performance.

基于聚类分析的图像分割方法具有样本空间约束小，通用性强的优点。模糊C-均值聚类算法作为一种无监督聚类算法，在实际工程中得到了广泛的应用。但是，仍然存在一些缺陷：模糊C均值聚类算法难以有效地解释噪声，这使其对噪声更加敏感，关键参数的选择必须通过反复试验来进行，从而减少了噪声。算法的适应性。此外，其迭代过程受初始聚类中心的影响很大，很容易陷入局部最优状态。因此，本文提出了一种基于局部信息权重的直觉模糊C均值聚类方法。通过引入本地信息权重，提出的算法在模糊分区中自适应地调整局部信息影响权重，增强了对噪声图像的鲁棒性。此外，提出了一种新的群体智能算法，称为“ Gold-Panning算法”，以优化初始聚类中心和聚类算法中的关键参数。通过利用Gold-Panning算法，进一步提高了所提出的聚类算法的适应性。在本文中，将对所提出的方法进行详细说明，并与现有方法进行比较，以证明其优越的性能。提出了优化聚类算法中初始聚类中心和关键参数的方法。通过利用Gold-Panning算法，进一步提高了所提出的聚类算法的适应性。在本文中，将对所提出的方法进行详细说明，并与现有方法进行比较，以证明其优越的性能。提出了优化聚类算法中初始聚类中心和关键参数的方法。通过利用Gold-Panning算法，进一步提高了所提出的聚类算法的适应性。在本文中，将对所提出的方法进行详细说明，并与现有方法进行比较，以证明其优越的性能。