Generalized local N-ary patterns for texture classification

Sheng Wang, Xiangjian He, Qiang Wu, Jie Yang

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

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.

Original languageEnglish
Title of host publication2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2013
PublisherIEEE Computer Society
Pages324-329
Number of pages6
ISBN (Print)9781479907038
DOIs
Publication statusPublished - 2013
Externally publishedYes
Event2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2013 - Krakow, Poland
Duration: 27 Aug 201330 Aug 2013

Publication series

Name2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2013

Conference

Conference2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2013
Country/TerritoryPoland
CityKrakow
Period27/08/1330/08/13

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing

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