An automatic segmentation technique for color images based on SOFM neural network
An automatic segmentation technique for color images based on SOFM neural network
复制标题
DOI：
10.1109/ijcnn.2009.5178725
复制
发表时间：
2009-06
期刊：
影响因子：
--
通讯作者：
Jun Zhang;Jinglu Hu
中科院分区：
文献类型：
--
作者：
Jun Zhang;Jinglu Hu
提出了一种基于自组织特征映射(SOFM)神经网络的彩色图像自动分割方法。首先，使用二叉树聚类过程对图像中的颜色进行聚类。在树的每个节点中，使用SOFM神经网络作为分类器，该分类器由图像颜色值提供。SOFM神经网络的输出神经元定义了每个节点的颜色类别。在我们的方法中，每个节点的颜色类的数量是两个。对于树的每个节点，使用基于霍特林变换的分割条件来定义是否应该分割当前的颜色类。为了提高整个算法的速度，采用最近邻插值法得到SOFM神经网络的小训练集。一旦图像中的颜色被聚类，就很容易通过分析图像中的颜色特征来分割目标。该方法与配色方案无关，适用于任何类型的彩色图像。实验结果表明了该方法的有效性。
In this paper, an automatic segmentation method based on self-organizing feature map (SOFM) neural network (NN) is presented for color images. First, a binary tree clustering procedure is used to cluster the colors in an image. In each node of the tree, a SOFM NN is used as a classifier which is fed by image color values. The output neurons of the SOFM NN define the color classes for each node. In our method, the number of color classes for each node is two. For each node of the tree, Hotelling transform based splitting condition is used to define if the current color classes should be split. To speed up the entire algorithm, a nearest neighbor interpolation is used to get the small training set for SOFM NN. Once the colors in an image are clustered, it is easy to segment a target by analyzing the color feature in an image. The method is independent of the color scheme, so it is applicable to any type of color images. Our experimental results show the validity of the proposed method.