Vector quantization of images using modified adaptive resonance algorithm for hierarchical clustering

Author

Vlajic, Natalija ; Card, Howard C.

Author_Institution

Sch. of Inf. Technol. & Eng., Ottawa Univ., Ont., Canada

Volume

12

Issue

5

fYear

2001

fDate

9/1/2001 12:00:00 AM

Firstpage

1147

Lastpage

1162

Abstract

A modified adaptive resonance theory (ART2) learning algorithm, which we employ in this paper, belongs to the family of NN algorithms whose main goal is the discovery of input data clusters, without considering their actual size. This feature makes the modified ART2 algorithm very convenient for image compression tasks, particularly when dealing with images with large background areas containing few details. Moreover, due to the ability to produce hierarchical quantization (clustering), the modified ART2 algorithm is proved to significantly reduce the computation time required for coding, and therefore enhance the overall compression process. Examples of the results obtained are presented, suggesting the benefits of using this algorithm for the purpose of VQ, i.e., image compression, over the other NN learning algorithms

Keywords

ART neural nets; image coding; pattern clustering; unsupervised learning; vector quantisation; ART2 neural network; adaptive resonance algorithm; hierarchical clustering; image coding; image compression; unsupervised learning; vector quantization; Clustering algorithms; Data compression; Decoding; Entropy; Image coding; Image reconstruction; Minimization methods; Neural networks; Resonance; Vector quantization;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/72.950143

Filename

950143