Encoder-segmented neural network (ESNN) for image segmentation

Neural networks have been applied to many kinds of image processing with well performance. When dealing with the large image, a large number of neurons is required so as to (i)make the construction model more complex (ii)make the speed of processing slower than the traditional methods due to heavy computation load. In this paper, an encoder-segmented neural network (ESNN) is constructed for image segmentation in which the available data can be obtained by a weight matrix containing maximum region information when a large number of input data are compressed by encoder network, meantime, the fuzzy clustering strategy applied on Hopfield neural network for the fine segmentation eliminates the tedious work of finding weighting factors. The experimental results indicate the performance of image segmentation can be improved effectively.