Adaptive FKCN method 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 for its integration of the fuzzy c-means (FCM) conception into the learning mechanism of Kohonen network. But there are many defects, for example, the number of network nodes can't be determined automatically, the speed of network convergence is very slow, and the computation cost is too large, when using FKCN to segment images. In order to overcome these defects, an adaptive FKCN model is presented, which can determine the network structure automatically according to the gray level distribution character 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 segmentation result is also improved.