The University of Southampton
University of Southampton Institutional Repository

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.

Text
shutao_PRL2004.pdf - Other
Download (576kB)

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

Catalogue record

Date deposited: 06 Sep 2004
Last modified: 25 Nov 2019 21:07

Export record

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×