Color Quantization Based on PCA and Kohonen SOFM

摘要

A method for initializing optimally Kohonen's Self-Organizing Feature Maps (SOFM) of a fixed zero neighborhood radius for use in color quantization is presented. Standard SOFM is applied to the projection of the input image pixels onto the plane spanned by the two largest principal components and to pixels of the original image defined by the smallest principal component via a thresholding procedure. The neuron values which emerge initialize the final SOFM of a fixed zero neighborhood radius that performs the color quantization of the original image. Experimental results show that the proposed method is able to produce smaller quantization errors than standard SOFM and other existing color quantization methods.