Automatic Segmentation Technique for Color Images

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 proposed method, the number of color classes for each node is two. For each node of the tree, Hotelling transform based class condition is used to define if the current color classes need to be classified. 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 the target by analyzing the representing colors.  The method is independent of the color scheme, which means that it is applicable  to any type of color images. The experimental results show the validity of the proposed method