高清彩色图像分割的Mini-batch FCM算法研究

The Mini-batch FCM Algorithm for HD Color Image Segmentation

模糊C-均值(Fuzzy C-Means,FCM)聚类算法是一种基于划分的无监督聚类算法,也是较为常见的图像分割算法之一,该算法通过寻找0~1之间的模糊隶属度等级来进行图像分割,并通过在特征空间中寻找聚类中心来达到最小化目标函数的目的。它的局限性主要有实时性较差、初始聚类中心的设置对最终结果影响较大、未考虑空间因素导致抗噪性弱。本文将mini-batch方法应用到FCM算法中,加快了FCM算法的收敛速度,提高了算法的效率及时效性,一定程度上解决了当数据特征复杂、集合较大时,FCM算法的实时性不是很理想的问题,继而节省算法运行的时间。

Fuzzy C-Means(FCM) clustering algorithm is an unsupervised clustering algorithm based on partition. It is also one of the common image segmentation algorithms. This algorithm conducts image segmentation by looking for the fuzzy membership grade between 0 ~ 1. The objective function is minimized by finding the clustering center in the feature space. Its limitations mainly include poor real-time performance,large impact on the final results due to the setting of the initial clustering center,and weak noise resistance due to the absence of space factors. In this paper,the mini-batch method is applied to the FCM algorithm to accelerate the convergence speed of the FCM algorithm,improve the efficiency and timeliness of the algorithm,and to some extent solve the problem that the real-time performance of the FCM algorithm is not ideal when the feature set of data is large,and then save the algorithm time.