Abstract:

BACKGROUND: Though the sole multi-fractal spectrum image segmentation has a great advantage when distinguishing edge texture, the results are influenced to be different when adopting different measures and thresholds when adopting different measures. So it is difficult to correctly adopt optimal measure.
OBJECTIVE: Combing the multi-fractal spectrum image segmentation method and self-organizing feature map neural network to process the image.
METHODS: The mean and variance of every pixel and others around were served as the basic characteristics. And the texture characteristics were combined with four different multi-fractal spectrums to realize self-organizing neural network.
RESULTS AND CONCLUSION: The results were different when adopting different measures and thresholds, meantime, the results were different to the different image using the same measure. So the key was how to choose the suitable measure. The method of multi-fractal spectrum image segmentation method binding self-organizing feature map neural network was used. It omitted the steps of choosing measures, and immediately made four multi-fractal spectrums be characteristics combining the other two characteristics to be the input of self-organizing neural network. Then the net was studied, and the image was automatically segmented. The experiment results showed that this method can fulfill effective segmentation in the complicated image with automatic and adaptively.