Color map image segmentation using optimized nearest neighbor classifiers

The author presents an optimized nearest neighbor rule based technique for extracting characters and lines from color geographic map images. In this method, the segmentation procedure is treated as a pattern classification problem. The author first obtains training samples interactively from characters, lines, and the background of an image. One can also produce training samples automatically using clustering algorithms. The author then generates a set of prototypes from the training samples and optimize the prototypes using a multilayer neural network to increase their classification power. The color image is classified pixel by pixel using the optimized prototypes. The method has been compared with adaptive thresholding with favorable results