A vector distribution model and an effective nearest neighbor search method for image vector quantization

L Guan; M Kamei

doi:10.1109/83.650121

A vector distribution model and an effective nearest neighbor search method for image vector quantization

IEEE Trans Image Process. 1997;6(12):1688-91. doi: 10.1109/83.650121.

Authors

L Guan¹, M Kamei

Affiliation

¹ NCR-Canada, Waterloo, Ont.

PMID: 18285238
DOI: 10.1109/83.650121

Abstract

In this correspondence, a modified version of Hunt's (1980) image model is used to interpret the distribution of image data vectors. The model suggests that the diagonal line of the coordinates system is a good approximation of the principal axis of the image data vector set. The validity of the model is supported by experiments. Following this suggestion, an effective nearest neighbor search method for vector quantization of image data is developed. The method is based on partitioning the vector space using hyperplanes which are perpendicular to the diagonal direction of the coordinate system. The validity of the method is assessed by analyzing its complexity and comparing its performance to those of existing algorithms on a number of images.