Vector quantization based on dynamic adjustment of Mahalanobis distance

Vector quantization (VQ) is widely used in many applications, ranging from image and speech coding to pattern recognition. The authorse propose a method for using the covariance matrix of the individual clusters as the basis for grouping. In this algorithm, the Mahalanobis distance is used as a measure of similarity in each cluster. Properties of the new clustering method are presented by examining the clustering quality for codebooks designed with the proposed method and two competing methods on a variety of data sets. The competing methods are the Linde-Buzo-Gray (LBG) algorithm and the fuzzy c-means (FCM) algorithm using Euclidean distance. The new method provides better results than the competing methods for several data sets. Thus, this method becomes another useful tool for use in codebook design.