Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Robustness-Driven Hybrid Descriptor for Noise-Deterrent Texture Classification
Univ Engn & Technol Taxila, Telecommun Engn Dept, ACTSENA Res Grp, Taxila 47050, Pakistan..
Univ Engn & Technol Taxila, Telecommun Engn Dept, ACTSENA Res Grp, Taxila 47050, Pakistan..ORCID iD: 0000-0001-8616-3959
Univ Engn & Technol Taxila, Telecommun Engn Dept, ACTSENA Res Grp, Taxila 47050, Pakistan..ORCID iD: 0000-0001-8416-5264
Show others and affiliations
2019 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 7, p. 110116-110127Article in journal (Refereed) Published
Abstract [en]

A robustness-driven hybrid descriptor (RDHD) for noise-deterrent texture classification is presented in this paper. This paper offers the ability to categorize a variety of textures under challenging image acquisition conditions. An image is initially resolved into its low-frequency components by applying wavelet decomposition. The resulting low-frequency components are further processed for feature extraction using completed joint-scale local binary patterns (CJLBP). Moreover, a second feature set is obtained by computing the low order derivatives of the original sample. The evaluated feature sets are integrated to get a final feature vector representation. The texture-discriminating performance of the hybrid descriptor is analyzed using renowned datasets: Outex original, Outex extended, and KTH-TIPS. The experimental results demonstrate a stable and robust performance of the descriptor under a variety of noisy conditions. An accuracy of 95.86%, 32.52%, and 88.74% at noise variance of 0.025 is achieved for the given datasets, respectively. A comparison between performance parameters of the proposed paper with its parent descriptors and recently published paper is also presented.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC , 2019. Vol. 7, p. 110116-110127
Keywords [en]
Feature descriptor, texture classification, Gaussian derivatives, wavelet decomposition, local binary pattern, noise robust
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-257833DOI: 10.1109/ACCESS.2019.2932687ISI: 000482001200017OAI: oai:DiVA.org:kth-257833DiVA, id: diva2:1348832
Note

QC 20190905

Available from: 2019-09-05 Created: 2019-09-05 Last updated: 2019-09-05Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full text

Authority records BETA

Tenhunen, Hannu

Search in DiVA

By author/editor
FawadKhan, Muhammad JamilRiaz, Muhammad AliAmin, YasarTenhunen, Hannu
By organisation
Electronics
In the same journal
IEEE Access
Electrical Engineering, Electronic Engineering, Information Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 10 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf