Vector quantization and clustering: a pyramid approach

Author

Tamir, Dan E. ; Park, Chi-Yeon ; Yoo, Wook-Sung

Author_Institution

Comput. Sci. Program, Florida Inst. of Technol., Melbourne, FL, USA

fYear

1995

fDate

28-30 Mar 1995

Firstpage

482

Abstract

A multi-resolution K-means clustering method is presented. Starting with a low resolution sample of the input data the K-means algorithm is applied to a sequence of monotonically increasing-resolution samples of the given data. The cluster centers obtained from a low resolution stage are used as initial cluster centers for the next stage which is a higher resolution stage. The idea behind this method is that a good estimation of the initial location of the cluster centers can be obtained through K-means clustering of a sample of the input data. K-means clustering of the entire data with the initial cluster centers estimated by clustering a sample of the input data, reduces the convergence time of the algorithm

Keywords

convergence of numerical methods; image coding; image resolution; image sampling; vector quantisation; K-means algorithm; cluster centers; convergence time; data sequence; image coding; input data; iteration; low resolution sample; monotonically increasing-resolution samples; multi-resolution K-means clustering method; pyramid approach; vector quantization; Algorithm design and analysis; Clustering algorithms; Clustering methods; Computer science; Convergence; Distortion measurement; Mean square error methods; Pixel; Testing; Vector quantization;

fLanguage

English

Publisher

ieee

Conference_Titel

Data Compression Conference, 1995. DCC '95. Proceedings

Conference_Location

Snowbird, UT

ISSN

1068-0314

Print_ISBN

0-8186-7012-6

Type

conf

DOI

10.1109/DCC.1995.515592

Filename

515592