图像分割的自适应FKCN方法

模糊Kohonen聚类网络(FKCN)是一种自组织模糊神经网络,由于它巧妙地将模糊c-均值(FCM)的概念引入Kohonen网络的学习机制中,所以在处理图像中广泛存在的模糊性和不确定性时表现出强大的优势.但将它用于图像分割时却存在着许多缺陷,如网络节点无法自动确定、网络收敛速度慢、计算量大等,从而使FKCN的应用受到限制.针对这些问题,本文提出了一种能根据目标图像的灰度分布特征自动确定网络结构的自适应FKCN算法.通过采用新的模糊算子及在网络学习过程中变换迭代样本空间,大大加快了网络的收敛速度、改善了分割结果.

Adaptive FKCN Method for Image Segmentation

Fuzzy Kohonen clustering network(FKCN) is a kind of self organizing fuzzy neural network.It shows great super iority in processing the ambiguity and uncertainty of image for its integration of the fuzzy c means(FCM) conception into the learning mechanism of Kohonen network.But there are many defects such as the number of network nodes can't be determined automatically,the speed of network convergence is very slow,and the computation cost is too large,when using FKCN to segment images.To over come these defects,an adaptive FKCN model is presented in this paper,which can determine the network structure automatically accor ding to the gray level distribution character of the image.By using the new fuzzy intensification operator and implementing a sample space transition in the network learning procedure,the network convergence speed is greatly improved and the segmentation result is also improved.