摘要
提出一种新的结构自适应免疫抗体竞争网络,无须预先设定聚类数目,实现了完全非监督的图像分割.基于自组织特征映射神经网络的基本概念,提出一个新的免疫抗体邻域概念,增强了网络的鲁棒性.根据大脑皮层长期记忆的形成原理提出一个长期记忆因子,提高了算法收敛的速度.为了抑制噪声抗原对抗体网络的影响,提出3种抗体死亡操作.以上这些改进措施可使生成的抗体网络更好地反映抗原的分布特征,得到自适应的网络结构.将此算法用于合成纹理图像、遥感图像和合成孔径雷达图像的分割,都取得了较好的分割结果.
This paper proposes a fully unsupervised image segmentation algorithm by using a novel structural adaptation artificial immune antibody competitive network without a predefined number of clustering. Based on the basic conception of self organizing feature map, a new immune antibody neighborhood is presented to enhance the robustness of the network, and inspired by the long-term memory in cerebral cortices, a long-term memory coefficient is introduced into the network to improve the convergence speed of the algorithm, and three death operations are presented to eliminate those antibody droves by noise antigen. With above advanced methods, the model can adaptively map input data into the antibody output space, which has a better adaptive net structure. This approach is applied to segment a variety of images into homogeneous regions, including synthetic texture images, remote images and SAR images, and experimental results illustrate the effectiveness of the proposed novel algorithm.
出处
《西安电子科技大学学报》
EI
CAS
CSCD
北大核心
2008年第3期444-448,494,共6页
Journal of Xidian University
基金
国家自然科学基金资助(60703107,60703108)
国家“863”计划资助(2006AA01Z107,2007AA12Z136,2007AA12Z223)
陕西省自然科学基金资助(2007F32)
关键词
非监督图像分割
人工免疫网络
结构自适应
数据聚类
unsupervised image segmentation
artificial immune networks
structural adaptation
data clustering
