CAJ | 학술논문

分类数和初始聚类中心的选取对红外图像的分割结果有较大的影响。传统的模糊C均值算法的分类数和聚类中心往往设定为经验值。为获得最佳的分类数，提出采用轮廓指标确定出较理想的分类数。针对传统的模糊C均值聚类算法对初始聚类中心比较敏感的问题，提出了基于直方图灰度值的最小最大距离法来确定初始聚类中心。实验结果表明该方法有效可行。
분류수화초시취류중심적선취대홍외도상적분할결과유교대적영향。전통적모호C균치산법적분류수화취류중심왕왕설정위경험치。위획득최가적분류수，제출채용륜곽지표학정출교이상적분류수。침대전통적모호C균치취류산법대초시취류중심비교민감적문제，제출료기우직방도회도치적최소최대거리법래학정초시취류중심。실험결과표명해방법유효가행。
The numbers of clusters and clustering center have a great effect on the selection of image segmentation results. The traditional Fuzzy C-Means algorithm always adopts empirical values as the number of clusters and clustering center. In order to determine the optimal number of clusters, the validity measure variable of Global Silhouette Index is employed. The most important disadvantage of Fuzzy C-Means clustering is that it generally does not give proper clustering center in the first run. A method which is the minimum-maximum distance based on the gray value of histogram to compute the original clustering center is put forward. Experimental results show that the method is effective and efficient.