A New Vector Quantization Clustering Algorithm

Abstract

The Pairwise Nearest Neighbor (PNN) algorithm is presented as an alternative to the Linde-Buzo-Gray (generalized Lloyd) algorithm for vector quantization clustering. The PNN algorithm derives a vector quantization codebook in a diminishingly small fraction of the time previously required, without sacrificing performance. In addition, the time needed to generate a codebook grows only like O (N log N) in training set size, and is Independent of the number of code words desired. Using this new method, one can either minimize the number of code words needed subject to a maximum allowable distortion or minimize the distortion subject to a maximum rate. The PNN algorithm can be used with squared error and weighted squared error distortion measures. Simulations on a variety of images encoded at 1/2 bit per pixel indicate that PNN codebooks can be developed in roughly 5 percent of the time required by the LBG algorithm. © 1989 IEEE