Statistical wavelet subband modelling for texture classification

PR Hill; CN Canagarajah; DR Bull

doi:10.1109/ICIP.2001.958979

Statistical wavelet subband modelling for texture classification

PR Hill
, CN Canagarajah
, DR Bull

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

1 Citation (Scopus)

393 Downloads (Pure)

Abstract

Simple wavelet and wavelet packet transforms have often been used for texture characterisation through the analysis of spatial-frequency content. However, most previous methods make no use of any statistical analysis of the transforms' subbands. A novel method is now presented for modelling the multivariate distributions of subband coefficients by considering spatially related coefficients. The Bhattacharya and divergence metrics are then used to produce an improved texture classification method for the application to content based image retrieval.

Translated title of the contribution	Statistical Wavelet Subband Modelling for Texture Classification
Original language	English
Title of host publication	ICIP 2001
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	165 - 168
Volume	1
ISBN (Print)	0780367251
DOIs	https://doi.org/10.1109/ICIP.2001.958979
Publication status	Published - Oct 2001
Event	International Conference on Image Processing, 2001 (ICIP 2001) - Thessaloniki, Greece Duration: 1 Oct 2001 → …

Conference

Conference	International Conference on Image Processing, 2001 (ICIP 2001)
Country/Territory	Greece
City	Thessaloniki
Period	1/10/01 → …

Bibliographical note

Rose publication type: Conference contribution

Terms of use: Copyright © 2001 IEEE. Reprinted from International Conference on Image Processing, 2001 (ICIP2001).

This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Bristol's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected].

By choosing to view this document, you agree to all provisions of the copyright laws protecting it.

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title = "Statistical wavelet subband modelling for texture classification",

abstract = "Simple wavelet and wavelet packet transforms have often been used for texture characterisation through the analysis of spatial-frequency content. However, most previous methods make no use of any statistical analysis of the transforms' subbands. A novel method is now presented for modelling the multivariate distributions of subband coefficients by considering spatially related coefficients. The Bhattacharya and divergence metrics are then used to produce an improved texture classification method for the application to content based image retrieval.",

author = "PR Hill and CN Canagarajah and DR Bull",

note = "Rose publication type: Conference contribution Terms of use: Copyright {\textcopyright} 2001 IEEE. Reprinted from International Conference on Image Processing, 2001 (ICIP2001). This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Bristol's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it. ; International Conference on Image Processing, 2001 (ICIP 2001) ; Conference date: 01-10-2001",

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AU - Canagarajah, CN

AU - Bull, DR

N1 - Rose publication type: Conference contribution Terms of use: Copyright © 2001 IEEE. Reprinted from International Conference on Image Processing, 2001 (ICIP2001). This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Bristol's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.

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N2 - Simple wavelet and wavelet packet transforms have often been used for texture characterisation through the analysis of spatial-frequency content. However, most previous methods make no use of any statistical analysis of the transforms' subbands. A novel method is now presented for modelling the multivariate distributions of subband coefficients by considering spatially related coefficients. The Bhattacharya and divergence metrics are then used to produce an improved texture classification method for the application to content based image retrieval.

AB - Simple wavelet and wavelet packet transforms have often been used for texture characterisation through the analysis of spatial-frequency content. However, most previous methods make no use of any statistical analysis of the transforms' subbands. A novel method is now presented for modelling the multivariate distributions of subband coefficients by considering spatially related coefficients. The Bhattacharya and divergence metrics are then used to produce an improved texture classification method for the application to content based image retrieval.

U2 - 10.1109/ICIP.2001.958979

DO - 10.1109/ICIP.2001.958979

M3 - Conference Contribution (Conference Proceeding)

SN - 0780367251

VL - 1

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EP - 168

BT - ICIP 2001

PB - Institute of Electrical and Electronics Engineers (IEEE)

T2 - International Conference on Image Processing, 2001 (ICIP 2001)

Y2 - 1 October 2001

ER -

Statistical wavelet subband modelling for texture classification

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