Hm. Abbas et Mm. Fahmy, CLASSIFIED VECTOR QUANTIZATION USING VARIANCE CLASSIFIER AND MAXIMUM-LIKELIHOOD CLUSTERING, Pattern recognition letters, 15(1), 1994, pp. 49-55
Citations number
15
Categorie Soggetti
Computer Sciences, Special Topics","Computer Science Artificial Intelligence
A classified vector image quantizer is proposed here. The algorithm em
ploys a one-feature variance classifier. This classifier has good prop
erties as it sorts the classes by its entropy contents. Then every cla
ss is clustered using the mixture maximum likelihood criterion instead
of the Euclidean distance. This shows that the number of clusters req
uired to represent any class can be determined. It also provides bette
r clustering by emphasizing on fitting a model of a mixture of Gaussia
n distributions by the data. Impressive results regarding the image qu
ality and bit rates are obtained when the algorithm is applied to imag
e compression.