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首页> 外文会议>International Conference on Artificial Intelligence and Pattern Recognition >Image Segmentation Based on Modified FCM Algorithms
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Image Segmentation Based on Modified FCM Algorithms

机译：基于修改的FCM算法的图像分割

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摘要

In this paper, two image segmentation methods, namely Genetic Simulate based FCM (Fuzzy C-Means, FCM) image segmentation and Rough Set based FCM image segmentation, are proposed. In the first methods, the FCM Clustering algorithm, Simulated Annealing algorithm (Simulated annealing, SA) and Genetic algorithm (Genetic algorithm, GA) are combined to overcome the drawbacks of conventional FCM segmentation algorithm, namely slow computation speed and over-dependence on initial value. In this method, the fuzzy cluster center is coded as a variable length chromosome, genetic operators such as intercross and mutation are introduced into a Simulated Annealing algorithm as an enhancement, which allows to recombine solutions produced by individual simulate annealing processes at fixed time intervals. At the same time Metropolis criterion is taken as a standard for a genetic operation to accept crossover and mutated individuals, this improves the convergence of the algorithm. Owing to the complementarities of FCM, SA and GA, this modified algorithm not only can escape from local minima but also holds higher parallel clustering segmentation capability concurrently. In the second method, Rough Set theory is used to optimal the performance of FCM in analyzing vagueness and uncertainty inherent in building clustering set. By reduction technique (the core of Rough Sets), those redundant initial cluster centers in the initial cluster set are eliminated, this is very useful for improving the convergence of the FCM algorithm. Experimental results demonstrate the efficiency and the effectiveness of the proposed methods.
机译：在本文中，两个图像分割方法，即遗传模拟基于FCM（模糊C均值，FCM）的图像分割和粗集基于FCM图像分割，提出了建议。在第一种方法中，FCM聚类算法，模拟退火算法（模拟退火，SA）和遗传算法（遗传算法，GA）相结合，以克服常规FCM分割算法，即慢的计算速度和初始过度依赖的缺点价值。在该方法中，模糊聚类中心被编码为可变长度染色体，遗传算如相互交叉和突变被引入到一个模拟退火算法的增强，其允许重组通过在固定的时间间隔从个人模拟退火方法生产的解决方案。与此同时Metropolis准则被视为一个标准的遗传操作，接受交叉和变异的个体，这提高了算法的收敛。由于FCM，SA和GA的互补性，这种改进算法不仅可以从局部最小值逃避，但也拥有更高的并行集群分割能力兼任。在第二种方法中，粗糙集理论是用来优化在分析模糊和建立集群一套固有的不确定性FCM的性能。通过还原技术（粗糙集的核心中），在初始簇集的多余初始聚类中心被消除，这是用于提高FCM算法的收敛非常有用的。实验结果表明，效率和所提出的方法的有效性。

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