A Fully Unsupervised Texture Segmentation Algorithm
A Fully Unsupervised Texture Segmentation Algorithm
This paper presents a fully unsupervised texture segmentation algorithm by using a modified discrete wavelet frames decomposition and a mean shift algorithm. By fully unsupervised, we mean the algorithm does not require any knowledge of the type of texture present nor the number of textures in the image to be segmented. The basic idea of the proposed method is to use the modified discrete wavelet frames to extract useful information from the image. Then, starting from the lowest level, the mean shift algorithm is used together with the fuzzy c-means clustering to divide the data into an appropriate number of clusters. The data clustering process is then refined at every level by taking into account the data at that particular level. The final crispy segmentation is obtained at the root level. This approach is applied to segment a variety of composite texture images into homogeneous texture areas and very good segmentation results are reported.
Texture segmentation, discrete wavelet frames, mean-shift clustering, fuzzy clustering
519-528
Fauzi, Mohammad F. A.
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Lewis, Paul H.
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Harvey, Richard
4a98201d-e9d2-4f18-b7d0-b25f4ed474fd
Bangham, J. Andrew
cc07f11f-d618-4c71-87e6-8dec50edf95c
2003
Fauzi, Mohammad F. A.
5c9fc2e3-3432-4e58-9f8f-4d2a5b8f2c0b
Lewis, Paul H.
7aa6c6d9-bc69-4e19-b2ac-a6e20558c020
Harvey, Richard
4a98201d-e9d2-4f18-b7d0-b25f4ed474fd
Bangham, J. Andrew
cc07f11f-d618-4c71-87e6-8dec50edf95c
Fauzi, Mohammad F. A. and Lewis, Paul H.
(2003)
A Fully Unsupervised Texture Segmentation Algorithm.
Harvey, Richard and Bangham, J. Andrew
(eds.)
British Machine Vision Conference 2003, Norwich, United Kingdom.
09 - 11 Sep 2003.
.
Record type:
Conference or Workshop Item
(Other)
Abstract
This paper presents a fully unsupervised texture segmentation algorithm by using a modified discrete wavelet frames decomposition and a mean shift algorithm. By fully unsupervised, we mean the algorithm does not require any knowledge of the type of texture present nor the number of textures in the image to be segmented. The basic idea of the proposed method is to use the modified discrete wavelet frames to extract useful information from the image. Then, starting from the lowest level, the mean shift algorithm is used together with the fuzzy c-means clustering to divide the data into an appropriate number of clusters. The data clustering process is then refined at every level by taking into account the data at that particular level. The final crispy segmentation is obtained at the root level. This approach is applied to segment a variety of composite texture images into homogeneous texture areas and very good segmentation results are reported.
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Published date: 2003
Additional Information:
Event Dates: 9 - 11 September 2003
Venue - Dates:
British Machine Vision Conference 2003, Norwich, United Kingdom, 2003-09-09 - 2003-09-11
Keywords:
Texture segmentation, discrete wavelet frames, mean-shift clustering, fuzzy clustering
Organisations:
Web & Internet Science
Identifiers
Local EPrints ID: 258261
URI: http://eprints.soton.ac.uk/id/eprint/258261
PURE UUID: e2ddd25d-b6b2-4dc5-a6b0-13593f8e632d
Catalogue record
Date deposited: 18 Oct 2003
Last modified: 14 Mar 2024 06:06
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Contributors
Author:
Mohammad F. A. Fauzi
Author:
Paul H. Lewis
Editor:
Richard Harvey
Editor:
J. Andrew Bangham
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