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Texture Segmentation by Evidence Gathering

Texture Segmentation by Evidence Gathering
Texture Segmentation by Evidence Gathering
A new approach to texture segmentation is presented which uses Local Binary Pattern data to provide evidence from which pixels can be classified into texture classes. The proposed algorithm, which we contend to be the first use of evidence gathering in the field of texture classification, uses Generalised Hough Transform style R-tables as unique descriptors for each texture class and an accumulator is used to store votes for each texture class. Tests on the Brodatz database and Berkeley Segmentation Dataset have shown that our algorithm provides excellent results; an average of 86.9% was achieved over 50 tests on 27 Brodatz textures compared with 80.3% achieved by segmentation by histogram comparison centred on each pixel. In addition, our results provide noticeably smoother texture boundaries and reduced noise within texture regions. The concept is also a "higher order" texture descriptor, whereby the arrangement of texture elements is used for classification as well as the frequency of occurrence that is featured in standard texture operators. This results in a unique descriptor for each texture class based on the structure of texture elements within the image, which leads to a homogeneous segmentation, in boundary and area, of texture by this new technique.
texture, segmentation, evidence gathering, lbp
Waller, Ben
a802857c-78ee-4290-a400-20d9ef2b0f5e
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Carter, John
e05be2f9-991d-4476-bb50-ae91606389da
Waller, Ben
a802857c-78ee-4290-a400-20d9ef2b0f5e
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Carter, John
e05be2f9-991d-4476-bb50-ae91606389da

Waller, Ben, Nixon, Mark and Carter, John (2011) Texture Segmentation by Evidence Gathering. 3rd BMVC Workshop, United Kingdom.

Record type: Conference or Workshop Item (Other)

Abstract

A new approach to texture segmentation is presented which uses Local Binary Pattern data to provide evidence from which pixels can be classified into texture classes. The proposed algorithm, which we contend to be the first use of evidence gathering in the field of texture classification, uses Generalised Hough Transform style R-tables as unique descriptors for each texture class and an accumulator is used to store votes for each texture class. Tests on the Brodatz database and Berkeley Segmentation Dataset have shown that our algorithm provides excellent results; an average of 86.9% was achieved over 50 tests on 27 Brodatz textures compared with 80.3% achieved by segmentation by histogram comparison centred on each pixel. In addition, our results provide noticeably smoother texture boundaries and reduced noise within texture regions. The concept is also a "higher order" texture descriptor, whereby the arrangement of texture elements is used for classification as well as the frequency of occurrence that is featured in standard texture operators. This results in a unique descriptor for each texture class based on the structure of texture elements within the image, which leads to a homogeneous segmentation, in boundary and area, of texture by this new technique.

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More information

Published date: 2 September 2011
Additional Information: Event Dates: 02/09/2011
Venue - Dates: 3rd BMVC Workshop, United Kingdom, 2011-09-02
Keywords: texture, segmentation, evidence gathering, lbp
Organisations: Vision, Learning and Control

Identifiers

Local EPrints ID: 272744
URI: http://eprints.soton.ac.uk/id/eprint/272744
PURE UUID: 93b0862d-ce79-4e97-ac80-122890236ad0
ORCID for Mark Nixon: ORCID iD orcid.org/0000-0002-9174-5934

Catalogue record

Date deposited: 04 Sep 2011 21:03
Last modified: 06 Jun 2018 13:18

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Contributors

Author: Ben Waller
Author: Mark Nixon ORCID iD
Author: John Carter

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