Color image segmentation using Hopfield networks

Color image segmentation is frequently based on pixel classification, either supervised or unsupervised, without taking into account spatial information. This may generate noisy results. One technique proposed to solve this problem is the use of Hopfield neural networks. In this paper, we present two segmentation algorithms for color image segmentation based on Huang's idea of describing the segmentation problem as one of minimizing a suitable energy function for a Hopfield network. The first algorithm, which resembles Huang's algorithm for grey-level images, builds three different networks (one for each color feature considered), and then combines the results. The second builds a single network according to the number of clusters obtained by histogram analysis. We have changed the network initialization, its dynamic evolution, and the technique of histogram analysis employed in both with respect to the original proposition. The experimental results, heuristically and quantitatively evaluated, are encouraging.