A New Wavelet Completed Local Ternary Count (WCLTC) for Image Classification

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dc.contributor.authorRassem, Taha H.
dc.contributor.authorAlkareem, Fatimah A.
dc.contributor.authorMohammed, Mohammed Falah
dc.contributor.authorMakbol, Nasrin M.
dc.contributor.authorSallam, Amer
dc.date.accessioned2024-09-24T10:15:24Z
dc.date.available2024-09-24T10:15:24Z
dc.date.issued2021-11-01
dc.descriptionThis work was supported by Universiti Malaysia Pahang through the Research Grant Scheme (FRGS), Ministry of Higher Education, Grant RDU190173 and University Malaysia Pahang Grant RDU190333
dc.description.abstractTo overcome noise sensitivity and increase the discriminative quality of the Local Binary Pattern, a Completed Local Ternary Count (CLTC) was developed by combining the Local Ternary Pattern (LTP) with the Completed Local Binary Count (CLBC) (LBP). Furthermore, by integrating the proposed CLTC with the Redundant Discrete Wavelet Transform (RDWT) to construct a Wavelet Completed Local Ternary Count, the proposed CLTC’s discriminative property is improved (WCLTC). As a result, more accurate local texture feature capture inside the RDWT domain is possible. The proposed WCLTC is utilised to perform texture and medical image classification tasks. The WCLTC performance is evaluated using two benchmark texture datasets, CUReT and Outex, as well as three medical picture databases, 2D Hela, VIRUS Texture, and BR datasets. With several of these datasets, the experimental findings demonstrate a remarkable classification accuracy. In several cases, the WCLTC performance outperformed the prior descriptions. With the 2D Hela, CUReT, and Virus datasets, the WCLTC achieves the highest classification accuracy of 96.91%, 97.04 percent, and 72.89%, respectively.
dc.funderOther external funder (please detail below)
dc.funder.otherUniversiti Malaysia Pahang through the Research Grant Scheme (FRGS), Ministry of Higher Education, Grant RDU190173 and University Malaysia Pahang Grant RDU190333
dc.identifier.citationRassem, T.H. et al. (2021) A New Wavelet Completed Local Ternary Count (WCLTC) for Image Classification. 2021 International Conference on Intelligent Technology, System and Service for Internet of Everything (ITSS-IoE),
dc.identifier.doihttps://doi.org/10.1109/itss-ioe53029.2021.9615301
dc.identifier.isbn9781665433051
dc.identifier.urihttps://hdl.handle.net/2086/24248
dc.language.isoen
dc.peerreviewedYes
dc.projectidRDU190173 and RDU190333
dc.publisherIEEE
dc.relation.ispartof2021 International Conference on Intelligent Technology, System and Service for Internet of Everything (ITSS-IoE)
dc.researchinstituteInstitute of Digital Research, Communication and Responsible Innovation
dc.rightsAttribution 2.0 UK: England & Walesen
dc.rights.urihttp://creativecommons.org/licenses/by/2.0/uk/
dc.titleA New Wavelet Completed Local Ternary Count (WCLTC) for Image Classification
dc.typeConference
oaire.citation.volume19

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