基于遗传模糊C-均值聚类算法的地图分割

图像分割和对象提取是从图像处理到图像分析的关键步骤。经典的模糊C-均值聚类算法(FCMA)是将图像分割成C类的常用方法，但依赖于初始聚类中心的选择。该算法通常得到的是局部最优解而非全局最优解。遗传算法是一类全局优化搜索算法。通过将遗传算法(GA)与FCMA相结合，对彩色地图直接按红绿蓝(RGB)三色空间进行聚类，用遗传算法搜索全局最优解，有效地避免了模糊C-均值聚类算法收敛到局部最优的问题，并在此基础上实现了对彩色地图的分割，得到了比较满意的效果。

Topographic Map Segmentation based on Genetic Fuzzy C - Means Clustering Algorithm

Image segmentation is the key step in image process.The class fuzzy c-means clustering algorithm(FCMA) is a well-known clustering method to partition an image into homogeneous region.However ,FCMA is dependent on the choice of the initial distribution of cluster center and consequently then algorithm ends up in a local optimum.In this paper,by applying genetic algorithm,a global optimization search algorithm to FCMA ,we can achieve then global optimum and have applied the algorithm to color topographic map segmentation.