CAJ | 학술논문

FCM用于彩色图像分割存在聚类数目需要事先确定、计算速度慢的问题,为此,提出一种快速的模糊C均值聚类方法(FFCM).首先.对原始彩色图像进行基于梯度图的分水岭变换,从而把原始彩色图像数据分成一些具有色彩一致性的子集;然后,利用这些子集的大小和中心点进行模糊聚类.由于FFCM聚类样本数量显著减小,因此可以大幅提高模糊C均值聚类算法的计算速度,进而可以采用聚类有效性指标确定聚类数目.实验表明,这种方法不需要事先确定聚类数目,在聚类有效性能不变的前提下,可以使模糊聚类的速度得到明显提高,实现了彩色图像的快速分割.
FCM용우채색도상분할존재취류수목수요사선학정、계산속도만적문제,위차,제출일충쾌속적모호C균치취류방법(FFCM).수선.대원시채색도상진행기우제도도적분수령변환,종이파원시채색도상수거분성일사구유색채일치성적자집;연후,이용저사자집적대소화중심점진행모호취류.유우FFCM취류양본수량현저감소,인차가이대폭제고모호C균치취류산법적계산속도,진이가이채용취류유효성지표학정취류수목.실험표명,저충방법불수요사선학정취류수목,재취류유효성능불변적전제하,가이사모호취류적속도득도명현제고,실현료채색도상적쾌속분할.
A fast fuzzy C-means clustering algorithm for color image segmentation is proposed to solve the problem of heavy calculating burden and the disadvantage that it needs the number of clusters for FCM.h uses watershed transform that is based on gradient image to partition the color image data into a certain number of subsets with similar color firstly,and then centers and sizes of the subsets are used in FCM.The computation speed of the fuzzy clustering algorithm is improved greatly because the number of color image data points used in fuzzy clustering is reduced notably.Furthermore,it can use the cluster validity in-dex to find the number of clusters quick] y.Exporiments show that without changing the clustering function,the proposed approach has much faster computation speed than FCM algorithm and can segment the color image quickly and effectively.