Adaptive fuzzy Kohonen clustering network for image segmentation

Fuzzy Kohonen clustering network (FKCN) is a kind of self-organizing fuzzy neural network. It shows great superiority in processing the ambiguity and uncertainty of image, but it encounters some difficulties when used for image segmentation. To overcome these defects, an adaptive FKCN model is presented in this paper, which can determine the network structure automatically according to the gray level distribution of the image. By using the new fuzzy intensification operator and implementing a sample space transition in the network learning procedure, the network convergence speed is greatly improved and the computation cost of image segmentation is significantly decreased