Gaussian mixture models of texture and colour for image database retrieval

H. Permuter; J. Francos; I. H. Jermyn

Gaussian mixture models of texture and colour for image database retrieval

H. Permuter, J. Francos, I. H. Jermyn

Department of Electrical & Computer Engineering

Research output: Contribution to journal › Conference article › peer-review

97 Scopus citations

Abstract

We introduce Gaussian mixture models of 'structure' and colour features in order to classify coloured textures in images, with a view to the retrieval of textured colour images from databases. Classifications are performed separately using structure and colour and then combined using a confidence criterion. We apply the models to the VisTex database and to the classification of man-made and natural areas in aerial images. We compare these models with others in the literature, and show an overall improvement in performance.

Original language	English
Pages (from-to)	569-572
Number of pages	4
Journal	Proceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing
Volume	3
State	Published - 1 Jan 2003
Event	2003 IEEE International Conference on Accoustics, Speech, and Signal Processing - Hong Kong, Hong Kong Duration: 6 Apr 2003 → 10 Apr 2003

ASJC Scopus subject areas

Software
Signal Processing
Electrical and Electronic Engineering

Cite this

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abstract = "We introduce Gaussian mixture models of 'structure' and colour features in order to classify coloured textures in images, with a view to the retrieval of textured colour images from databases. Classifications are performed separately using structure and colour and then combined using a confidence criterion. We apply the models to the VisTex database and to the classification of man-made and natural areas in aerial images. We compare these models with others in the literature, and show an overall improvement in performance.",

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AU - Permuter, H.

AU - Francos, J.

AU - Jermyn, I. H.

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N2 - We introduce Gaussian mixture models of 'structure' and colour features in order to classify coloured textures in images, with a view to the retrieval of textured colour images from databases. Classifications are performed separately using structure and colour and then combined using a confidence criterion. We apply the models to the VisTex database and to the classification of man-made and natural areas in aerial images. We compare these models with others in the literature, and show an overall improvement in performance.

AB - We introduce Gaussian mixture models of 'structure' and colour features in order to classify coloured textures in images, with a view to the retrieval of textured colour images from databases. Classifications are performed separately using structure and colour and then combined using a confidence criterion. We apply the models to the VisTex database and to the classification of man-made and natural areas in aerial images. We compare these models with others in the literature, and show an overall improvement in performance.

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M3 - Conference article

AN - SCOPUS:0141563846

SN - 1520-6149

VL - 3

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EP - 572

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

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

T2 - 2003 IEEE International Conference on Accoustics, Speech, and Signal Processing

Y2 - 6 April 2003 through 10 April 2003

ER -

Gaussian mixture models of texture and colour for image database retrieval

Abstract

ASJC Scopus subject areas

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