CAJ | 학술논문

首先在确定模糊聚类的初始聚类数和初始聚类中心方面，引入颜色直方图应用于FCM聚类算法中。其次再将空间信息引入到FCM中，重建包含邻域信息的新的隶属度迭代函数。最后，用内核诱导距离取代原算法中的欧式距离，对实验图像的特征进行优化，并对算法进行评价对比。实验结果表明，该算法具有良好的分割质量和效果，并且也具有较强大的噪声抑制能力。
수선재학정모호취류적초시취류수화초시취류중심방면，인입안색직방도응용우FCM취류산법중。기차재장공간신식인입도FCM중，중건포함린역신식적신적대속도질대함수。최후，용내핵유도거리취대원산법중적구식거리，대실험도상적특정진행우화，병대산법진행평개대비。실험결과표명，해산법구유량호적분할질량화효과，병차야구유교강대적조성억제능력。
First,we introduce and apply the colour histogram to FCM clustering algorithm to determine the initial number of clusters and initial cluster centres of the fuzzy clustering.Then we bring spatial information to FCM to reconstruct the new membership iteration function containing neighbourhood information.Finally,we replace the original Euclidean distance in original algorithm with kernel-induced distance, and optimise the features of experimental image.The algorithm is also evaluated and compared.Experimental results show:this algorithm hasa good quality and effect in image segmentation,and has a stronger anti-nose ability as well.