Paper Info
Title
Lloyd clustering of Gauss mixture models for image compression and classification
Abstract
Gauss mixtures have gained popularity in statistics and statistical signal processing applications for a variety of reasons, including their ability to well approximate a large class of interesting densities and the availability of algorithms such as the Baum–Welch or expectation-maximization (EM) algorithm for constructing the models based on observed data. We here consider a quantization approach to Gauss mixture design based on the information theoretic view of Gaussian sources as a “worst case” for robust signal compression. Results in high-rate quantization theory suggest distortion measures suitable for Lloyd clustering of Gaussian components based on a training set of data. The approach provides a Gauss mixture model and an associated Gauss mixture vector quantizer which is locally robust. We describe the quantizer mismatch distortion and its relation to other distortion measures including the traditional squared error, the Kullback–Leibler (relative entropy) and minimum discrimination information, and the log-likehood distortions. The resulting Lloyd clustering algorithm is demonstrated by applications to image vector quantization, texture classification, and North Atlantic pipeline image classification.
Year
DOI
Venue
2005
10.1016/j.image.2005.03.003
Signal Processing: Image Communication
Keywords
Field
DocType
Clustering,Compression,Quantization,Gauss mixture,Statistical classification,Segmentation
Gauss,Pattern recognition,Computer science,Vector quantization,Artificial intelligence,Cluster analysis,Quantization (signal processing),Distortion,Image compression,Kullback–Leibler divergence,Mixture model
Journal
Volume
Issue
ISSN
20
5
0923-5965
Citations 
PageRank 
References 
30
1.92
31
Authors
5
Name
Order
Citations
PageRank
Anuradha K. Aiyer1637.09
Kyungsuk Pyun2956.75
Ying-zong Huang3605.85
Deirdre B. O'brien4674.88
Robert M. Gray51197841.53