An Improved Local Ternary Pattern for Texture Classification

Huang Chia Shih, Hsu Yung Cheng, Jr Chian Fu

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

1 Scopus citations

Abstract

In this study, we proposed a new operator known as the synchronized rotation local ternary pattern (SRLTP) for texture classification. The proposed SRLTP descriptor improves on the local ternary pattern (LTP) method with an additional process on the generated lower and upper LTPs. The lower and upper patterns are encoded to a rotation invariant pattern histogram and a uniform pattern histogram, respectively. Thus, the feature vector can utilize the advantages offered by the rotation invariant pattern histogram while retaining the original information in the uniform pattern histogram. Moreover, in this study, a two-dimensional discrete wavelet transform (DWT) and a discrete Fourier transform (DFT) enhanced the robustness of the texture classification. The experimental results demonstrate that the performance of the SRLTP descriptor is better than those of the existing descriptors.

Original languageEnglish
Title of host publication2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings
PublisherIEEE Computer Society
Pages4415-4418
Number of pages4
ISBN (Electronic)9781538662496
DOIs
StatePublished - Sep 2019
Event26th IEEE International Conference on Image Processing, ICIP 2019 - Taipei, Taiwan
Duration: 22 Sep 201925 Sep 2019

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2019-September
ISSN (Print)1522-4880

Conference

Conference26th IEEE International Conference on Image Processing, ICIP 2019
Country/TerritoryTaiwan
CityTaipei
Period22/09/1925/09/19

Keywords

  • Local ternary pattern
  • rotation invariance
  • scale invariance
  • texture classification
  • texture representation

Fingerprint

Dive into the research topics of 'An Improved Local Ternary Pattern for Texture Classification'. Together they form a unique fingerprint.

Cite this