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

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 ima...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on neural networks Vol. 12; no. 5; pp. 1147 - 1162
Main Authors: Vlajic, N., Card, H.C.
Format: Journal Article
Language:English
Published: United States IEEE 01.09.2001
Subjects:
Adaptive algorithms
Algorithms
Clustering
Clustering algorithms
Compressing
Data compression
Decoding
Entropy
Image coding
Image compression
Image reconstruction
Learning
Minimization methods
Neural networks
Resonance
Vector quantization
ISSN:1045-9227
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ObjectType-Article-2
ObjectType-Feature-1
ISSN:1045-9227
DOI:10.1109/72.950143