Using local features in a neural network based gray-level reduction technique

Proposes a method for reduction of the number of gray-levels in an image. The proposed approach achieves gray-level reduction using the image gray-levels and additional local spatial features. Both gray-level and local feature values feed a self-organized neural network classifier. The final image has not only the dominant image gray-levels, but also has texture approaching the image local characteristics used. To speed up the entire multithresholding algorithm and reduce memory requirements, a fractal scanning sub-sampling technique can be used