Context-based graphical modeling for wavelet domain signal processing

Nilanjan Dasgupta; Lawrence Carin

Context-based graphical modeling for wavelet domain signal processing

Nilanjan Dasgupta, Lawrence Carin

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Wavelet-domain hidden Markov tree (HMT) modeling provides a powerful approach to capture the underlying statistics of the wavelet coefficients. We develop a mutual information-based information-theoretic approach to quantify the interactions between the wavelet coefficients within a wavelet tree. This graphical method enables the design of a context-specific hidden Markov tree (HMT) by adding or deleting links from the traditional tree structure. The performance of the model is demonstrated on segmenting two-dimensional synthetic textures having intricate substructures, although the method can be used for signals of arbitrary dimensions.

Original language	English (US)
Title of host publication	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Pages	485-488
Number of pages	4
State	Published - Sep 25 2003
Externally published	Yes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2021-02-09

Cite this

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title = "Context-based graphical modeling for wavelet domain signal processing",

abstract = "Wavelet-domain hidden Markov tree (HMT) modeling provides a powerful approach to capture the underlying statistics of the wavelet coefficients. We develop a mutual information-based information-theoretic approach to quantify the interactions between the wavelet coefficients within a wavelet tree. This graphical method enables the design of a context-specific hidden Markov tree (HMT) by adding or deleting links from the traditional tree structure. The performance of the model is demonstrated on segmenting two-dimensional synthetic textures having intricate substructures, although the method can be used for signals of arbitrary dimensions.",

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Y1 - 2003/9/25

N2 - Wavelet-domain hidden Markov tree (HMT) modeling provides a powerful approach to capture the underlying statistics of the wavelet coefficients. We develop a mutual information-based information-theoretic approach to quantify the interactions between the wavelet coefficients within a wavelet tree. This graphical method enables the design of a context-specific hidden Markov tree (HMT) by adding or deleting links from the traditional tree structure. The performance of the model is demonstrated on segmenting two-dimensional synthetic textures having intricate substructures, although the method can be used for signals of arbitrary dimensions.

AB - Wavelet-domain hidden Markov tree (HMT) modeling provides a powerful approach to capture the underlying statistics of the wavelet coefficients. We develop a mutual information-based information-theoretic approach to quantify the interactions between the wavelet coefficients within a wavelet tree. This graphical method enables the design of a context-specific hidden Markov tree (HMT) by adding or deleting links from the traditional tree structure. The performance of the model is demonstrated on segmenting two-dimensional synthetic textures having intricate substructures, although the method can be used for signals of arbitrary dimensions.

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M3 - Conference contribution

SP - 485

EP - 488

BT - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

ER -

Context-based graphical modeling for wavelet domain signal processing

Abstract

Bibliographical note

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