Generalized local N-ary patterns for texture classification

Wang, S; He, X; Wu, Q; Yang, J

Generalized local N-ary patterns for texture classification

Wang, S He, X

Wu, Q

Yang, J

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Publication Type:: Conference Proceeding
Citation:: 2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2013, 2013, pp. 324 - 329
Issue Date:: 2013-01-01

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Field	Value	Language
dc.contributor.author	Wang, S	en_US
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X	en_US
dc.contributor.author	Wu, Q https://orcid.org/0000-0001-5641-2483	en_US
dc.contributor.author	Yang, J	en_US
dc.date.issued	2013-01-01	en_US
dc.identifier.citation	2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2013, 2013, pp. 324 - 329	en_US
dc.identifier.isbn	9781479907038	en_US
dc.identifier.uri	http://hdl.handle.net/10453/26606
dc.description.abstract	Local Binary Pattern (LBP) has been well recognised and widely used in various texture analysis applications of computer vision and image processing. It integrates properties of texture structural and statistical texture analysis. LBP is invariant to monotonic gray-scale variations and has also extensions to rotation invariant texture analysis. In recent years, various improvements have been achieved based on LBP. One of extensive developments was replacing binary representation with ternary representation and proposed Local Ternary Pattern (LTP). This paper further generalises the local pattern representation by formulating it as a generalised weight problem of Bachet de Meziriac and proposes Local N-ary Pattern (LNP). The encouraging performance is achieved based on three benchmark datasets when compared with its predecessors. © 2013 IEEE.	en_US
dc.relation.ispartof	2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2013	en_US
dc.relation.isbasedon	10.1109/AVSS.2013.6636660	en_US
dc.title	Generalized local N-ary patterns for texture classification	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Krakow, Poland	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US

Abstract:

Local Binary Pattern (LBP) has been well recognised and widely used in various texture analysis applications of computer vision and image processing. It integrates properties of texture structural and statistical texture analysis. LBP is invariant to monotonic gray-scale variations and has also extensions to rotation invariant texture analysis. In recent years, various improvements have been achieved based on LBP. One of extensive developments was replacing binary representation with ternary representation and proposed Local Ternary Pattern (LTP). This paper further generalises the local pattern representation by formulating it as a generalised weight problem of Bachet de Meziriac and proposes Local N-ary Pattern (LNP). The encouraging performance is achieved based on three benchmark datasets when compared with its predecessors. © 2013 IEEE.

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http://hdl.handle.net/10453/26606