A color clustering technique for image segmentation
Computer Vision, Graphics, and Image Processing
Parallel image segmentation using modified Hopfield model
Pattern Recognition Letters - Special issue on artificial neural networks
Colour image segmentation by modular neural network
Pattern Recognition Letters
Self-organizing maps
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Markov Random Field Models for Unsupervised Segmentation of Textured Color Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature encoding for unsupervised segmentation of color images
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Adaptive color segmentation-a comparison of neural and statistical methods
IEEE Transactions on Neural Networks
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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.