基于近邻搜索花授粉优化的直觉模糊聚类图像分割

为克服传统模糊聚类算法应用于图像分割时，易受噪声影响，对聚类中心初始值敏感，易陷入局部最优，模糊信息处理能力不足等缺陷，该文提出基于近邻搜索花授粉优化的直觉模糊聚类图像分割算法。首先设计一种新颖的图像空间信息提取策略，进而构造融合图像空间信息的直觉模糊聚类目标函数，提高对于噪声的鲁棒性，提升算法处理图像中模糊信息的能力。为了优化上述目标函数，提出一种基于近邻学习搜索机制的花授粉算法，实现对于聚类中心的寻优，解决对于聚类中心初始值敏感，易陷入局部最优的问题。实验结果表明所提算法能在多种噪声图像上取得令人满意的分割效果。

Intuitionistic Fuzzy Clustering Image Segmentation Based on Flower Pollination Optimization with Nearest Neighbor Searching

In order to overcome shortcomings of the traditional fuzzy clustering algorithm for image segmentation, such as that are easily affected by noise, sensitive to the initial value of clustering center, easily falling into local optimum, and inadequate ability of fuzzy information processing, an intuitionistic fuzzy clustering image segmentation algorithm is proposed based on flower pollination optimization with nearest neighbor searching. Firstly, a novel extraction strategy of image spatial information is proposed, and then an intuitionistic fuzzy clustering objective function with image spatial information is constructed to improve the algorithm’s robustness against noise and enhance the ability of the algorithm to process the image fuzzy information. In order to overcome the defects of sensitivity to clustering centers and easily falling into local optimum, a flower pollination algorithm based on nearest neighbor learning search mechanism is proposed. Experimental results show that the proposed method can get satisfactory segmentation results on a variety of noisy images.