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Comparison and Fusion of Multiresolution Features for Texture Classification

Comparison and Fusion of Multiresolution Features for Texture Classification
Comparison and Fusion of Multiresolution Features for Texture Classification
In this paper, we investigate the texture classification problem with individual and combined multiresolution features, i.e., dyadic wavelet, wavelet frame, Gabor wavelet, and steerable pyramid. Support vector machines are used as classifiers. The experimental results show that the steerable pyramid and Gabor wavelet classify texture images with the highest accuracy, the wavelet frame follows them, the dyadic wavelet significantly lags behind. Experimental results on fused features demonstrated the combination of two feature sets always outperformed each method individually. And the fused feature sets of multi-orientation decompositions and stationary wavelet achieve the highest accuracy.
Multiresolution analysis, Texture classification, Support vector machines
Li, Shutao
1b13115f-764b-4a68-bb05-e3d4fe4b841f
Shawe-Taylor, John
b1931d97-fdd0-4bc1-89bc-ec01648e928b
Li, Shutao
1b13115f-764b-4a68-bb05-e3d4fe4b841f
Shawe-Taylor, John
b1931d97-fdd0-4bc1-89bc-ec01648e928b

Li, Shutao and Shawe-Taylor, John (2004) Comparison and Fusion of Multiresolution Features for Texture Classification. Pattern Recognition Letters, 25.

Record type: Article

Abstract

In this paper, we investigate the texture classification problem with individual and combined multiresolution features, i.e., dyadic wavelet, wavelet frame, Gabor wavelet, and steerable pyramid. Support vector machines are used as classifiers. The experimental results show that the steerable pyramid and Gabor wavelet classify texture images with the highest accuracy, the wavelet frame follows them, the dyadic wavelet significantly lags behind. Experimental results on fused features demonstrated the combination of two feature sets always outperformed each method individually. And the fused feature sets of multi-orientation decompositions and stationary wavelet achieve the highest accuracy.

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More information

Published date: 2004
Keywords: Multiresolution analysis, Texture classification, Support vector machines
Organisations: Electronics & Computer Science

Identifiers

Local EPrints ID: 259910
URI: http://eprints.soton.ac.uk/id/eprint/259910
PURE UUID: adfe1833-ff0d-4ccc-8809-b2479d205278

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Date deposited: 06 Sep 2004
Last modified: 14 Mar 2024 06:29

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Contributors

Author: Shutao Li
Author: John Shawe-Taylor

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