Combining image compression and classification using vectorquantization
Oehler, K.L.
Gray, R.M.
Integrated Syst. Lab., Texas Instrum. Inc., Dallas, TX;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: May 1995
Volume: 17,
Issue: 5
On page(s): 461-473
ISSN: 0162-8828
References Cited: 37
CODEN: ITPIDJ
INSPEC Accession Number: 4988885
Digital Object Identifier: 10.1109/34.391396
Current Version Published: 2002-08-06
Abstract
We describe a method of combining classification and compression
into a single vector quantizer by incorporating a Bayes risk term into
the distortion measure used in the quantizer design algorithm. Once
trained, the quantizer can operate to minimize the Bayes risk weighted
distortion measure if there is a model providing the required posterior
probabilities, or it can operate in a suboptimal fashion by minimizing
the squared error only. Comparisons are made with other vector quantizer
based classifiers, including the independent design of quantization and
minimum Bayes risk classification and Kohonen's LVQ. A variety of
examples demonstrate that the proposed method can provide classification
ability close to or superior to learning VQ while simultaneously
providing superior compression performance
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