Rotationally invariant texture classification

Paul R Hill; David Bull; CN Canagarajah

doi:10.1049/ic:20000569

Rotationally invariant texture classification

Paul R Hill
, David Bull
, CN Canagarajah

Research output: Contribution to journal › Article (Academic Journal) › peer-review

Abstract

Texture based features used for content based retrieval of images and videos should ideally be invariant to simple transforms such as rotation. This paper introduces the recently developed dual tree complex wavelet transform (DT-CWT) as a tool to extract rotationally invariant texture based features. When applied in two dimensions the DT-CWT produces shift invariant and orientated subbands at each decomposition scale. Rotationally invariant features can be extracted from the energies of these subbands whilst benefiting from the computational efficiency of the decomposition and the ability to choose the transform filters.

Original language	English
Pages (from-to)	20/1 - 20/5
Journal	IEE Seminar on Time-scale and Time-Frequency Analysis and Applications
DOIs	https://doi.org/10.1049/ic:20000569
Publication status	Published - Feb 2000

Bibliographical note

Sponsorship: This work was supported by the Virtual Centre of Excellence in Digital Broadcast and Multimedia Technology. The authors acknowledge the support and information provided by Dr N.G. Kingsbury of Cambridge
University.

Other identifier: Publ. No. 2000/019
Publisher: Institution of Electrical Engineers (IEE)

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http://hdl.handle.net/1983/1375

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title = "Rotationally invariant texture classification",

abstract = "Texture based features used for content based retrieval of images and videos should ideally be invariant to simple transforms such as rotation. This paper introduces the recently developed dual tree complex wavelet transform (DT-CWT) as a tool to extract rotationally invariant texture based features. When applied in two dimensions the DT-CWT produces shift invariant and orientated subbands at each decomposition scale. Rotationally invariant features can be extracted from the energies of these subbands whilst benefiting from the computational efficiency of the decomposition and the ability to choose the transform filters.",

author = "Hill, \{Paul R\} and David Bull and CN Canagarajah",

note = "Sponsorship: This work was supported by the Virtual Centre of Excellence in Digital Broadcast and Multimedia Technology. The authors acknowledge the support and information provided by Dr N.G. Kingsbury of Cambridge University. Other identifier: Publ. No. 2000/019 Publisher: Institution of Electrical Engineers (IEE)",

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AU - Hill, Paul R

AU - Bull, David

AU - Canagarajah, CN

N1 - Sponsorship: This work was supported by the Virtual Centre of Excellence in Digital Broadcast and Multimedia Technology. The authors acknowledge the support and information provided by Dr N.G. Kingsbury of Cambridge University. Other identifier: Publ. No. 2000/019 Publisher: Institution of Electrical Engineers (IEE)

PY - 2000/2

Y1 - 2000/2

N2 - Texture based features used for content based retrieval of images and videos should ideally be invariant to simple transforms such as rotation. This paper introduces the recently developed dual tree complex wavelet transform (DT-CWT) as a tool to extract rotationally invariant texture based features. When applied in two dimensions the DT-CWT produces shift invariant and orientated subbands at each decomposition scale. Rotationally invariant features can be extracted from the energies of these subbands whilst benefiting from the computational efficiency of the decomposition and the ability to choose the transform filters.

AB - Texture based features used for content based retrieval of images and videos should ideally be invariant to simple transforms such as rotation. This paper introduces the recently developed dual tree complex wavelet transform (DT-CWT) as a tool to extract rotationally invariant texture based features. When applied in two dimensions the DT-CWT produces shift invariant and orientated subbands at each decomposition scale. Rotationally invariant features can be extracted from the energies of these subbands whilst benefiting from the computational efficiency of the decomposition and the ability to choose the transform filters.

U2 - 10.1049/ic:20000569

DO - 10.1049/ic:20000569

M3 - Article (Academic Journal)

SN - 0963-3308

SP - 20/1 - 20/5

JO - IEE Seminar on Time-scale and Time-Frequency Analysis and Applications

JF - IEE Seminar on Time-scale and Time-Frequency Analysis and Applications

ER -

Rotationally invariant texture classification

Abstract

Bibliographical note

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