摘要
图像分割是图像处理和模式识别的重要课题,而图像特征空间聚类是图像分割的一种重要方法,认为图像的特征是图像中待分割物体表面所特有而且恒定的特征,并将图像的特征映射到某种几何空间,称为特征空间,并且假定图像中不同的待分割物体在该特征空间中呈现为不同的聚集.提出了自组织动态网络(SODNN)聚类算法,并且利用该算法对图像特征空间聚类.该算法实现了神经网络结构的快速生长和动态调节,具有自动适应数据内在分布特征和聚类结果更为准确稳定的特点.利用SODNN算法对图像颜色空间进行聚类的同时综合了图像的位置信息来实现图像分割.实验表明分割结果与人工分割结果具有较好的一致性.
Image segmentation is critical to image processing and pattern recognition, while feature space clustering is an important method for unsupervised image segmentation. The method assumes that image feature is a constant property of the surface of each object to be segmented within the image and the image feature could be mapped into a certain geometrical space called feature space. Meanwhile, the method also assumes that different objects present in the image will manifest themselves as different clusters in the feature space. Therefore, the problem of segmenting the objects of an image can be viewed as that of finding the mapping clusters in the feature space. Using a proposed novel competitive-learning-based neural network clustering algorithm to cluster image feature space, an unsupervised image segmentation method is realized. The proposed clustering algorithm, self-organizing dynamic neural net（SODNN）, is possible to dynamically grow swiftly and adjust the size of neural net more accurately, so it is able to find the number of clusters according to the input data pattern and get more stable and accurate result. Here, the SODNN algorithm is utilized and the color and position features are combined to carry out the image segmentation. The presented results show better consistency between the segmentation by proposed methods and the segmentation by human.
出处
《计算机研究与发展》
EI
CSCD
北大核心
2009年第1期23-30,共8页
Journal of Computer Research and Development
基金
国家自然科学基金项目(60435010)
国家“八六三”高技术研究发展计划基金项目(2006AA01Z128)
国家“九七三”重点基础研究发展规划基金项目(2007CB311004)~~
关键词
竞争学习
自组织
特征空间聚类
图像分割
图像特征
competitive learning
self-organizing
feature space clustering
image segmentation
image Feature
