Bayes Risk Weighted Vector Quantization With Posterior Estimation for Image Compression and Classification

Bayes Risk Weighted Vector Quantization With Posterior Estimation for Image Compression and Classification (1996)

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by K. O. Perlmutter , S. M. Perlmutter , R. M. Gray , R. A. Olshen , K. L. Oehler

Venue:	IEEE TRANSACTIONS ON IMAGE PROCESSING
Citations:	29 - 9 self

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@ARTICLE{Perlmutter96bayesrisk,
    author = {K. O. Perlmutter and S. M. Perlmutter and R. M. Gray and R. A. Olshen and K. L. Oehler},
    title = {Bayes Risk Weighted Vector Quantization With Posterior Estimation for Image Compression and Classification},
    journal = {IEEE TRANSACTIONS ON IMAGE PROCESSING},
    year = {1996},
    volume = {5},
    pages = {347--360}
}

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Abstract

Classification and compression play important roles in communicating digital information. Their combination is useful in many applications, including the detection of abnormalities in compressed medical images. In view of the similarities of compression and low-level classification, it is not surprising that there are many similar methods for their design. Because some of these methods are useful for designing vector quantizers, it seems natural that vector quantization (VQ) is explored for the combined goal. We investigate several VQ-based algorithms that seek to minimize both the distortion of compressed images and errors in classifying their pixel blocks. These algorithms are investigated with both full search and tree-structured codes. We emphasize a nonparametric technique that minimizes both error measures simultaneously by incorporating a Bayes risk component into the distortion measure used for design and encoding. We introduce a tree-structured posterior estimator to produce t...

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