Robust rotation invariant texture classification

RMS Porter, CN Canagarajah

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

352 Downloads (Pure)


The importance of texture analysis and classification in image processing is well known. However, many existing texture classification schemes suffer from a number of drawbacks. A large number of features are commonly used to represent each texture and an excessively large image area is often required for the texture analysis, both leading to high computational complexity. Furthermore, most existing schemes are highly orientation dependent and thus cannot correctly classify textures after rotation. In this paper, two novel feature extraction techniques for rotation invariant texture classification are presented. These schemes, using the wavelet transform and Gaussian Markov random field modelling, are shown to give a consistently high performance for rotated textures in the presence of noise. Moreover, they use just four features to represent each texture and require only a 16×16 image area for their analysis leading to a significantly lower computational complexity than most existing schemes
Translated title of the contributionRobust rotation invariant texture classification
Original languageEnglish
Title of host publicationUnknown
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages3157 - 3160
ISBN (Print)0818679190
Publication statusPublished - Apr 1997
Event1997 IEEE International Conference on Acoustics, Speech, and Signal Processing - Munich, Germany
Duration: 21 Apr 199724 Apr 1997


Conference1997 IEEE International Conference on Acoustics, Speech, and Signal Processing
Abbreviated titleICASSP '97

Bibliographical note

Conference Proceedings/Title of Journal: ICASSP
Rose publication type: Conference contribution

Terms of use: Copyright © 1997 IEEE. Reprinted from IEEE International Conference on Acoustics, Speech, and Signal Processing, 1997 (ICASSP-97).

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

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


Dive into the research topics of 'Robust rotation invariant texture classification'. Together they form a unique fingerprint.

Cite this