Rough sets neural network used in image segmentation

Abstract：The paper provides the method of the rough set neural network for image segmentation.The theory of rough reduction be used for the regional characteristics of segmented image, to reduce the dimension of feature vectors, extracted rules, then construct the number of hidden layer neurons by those rules.Finally, determine the structure of neural network, set input of each neuron in rough set neural network as values region, and output value as classification decision.Predetermined value of the right is the degree of membership rough rules, and then use BP iterative algorithm to the ultimate realize image segmentation.Experimental result indicated that the method greatly reduce the training time, improve the accuracy of the segmentation, and be superior to conventional images.It satisfies the requirements of real-time image processing.

Keyword：rough set;reduction;equivalence class;rough sets neural network;image segmentation