<title>Tree-structured vector quantization with input-weighted distortion measures</title>

Abstract

A greedy tree-growing algorithm is used in conjunction with an input-dependent weighted distortion measure to develop a tree-structured vector quantizer. Vectors in the training set are classified, and weights are assigned to the classes. The resulting weighted distortion measure forces the tree to develop better representations for those classes that are considered important. Results on medical images and USC database images are presented. A tree-structured vector quantizer grown in a similar manner can be used for preliminary classification as well as compression.