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

Rassem, T., Alkareem, F., Mohammed, M., Makbol, N. and Sallam, A, 2021. A New Wavelet Completed Local Ternary Count (WCLTC) for Image Classification. In: ITSS-IoE 2021: International Conference on Intelligent Technology, System and Service for Internet of Everything, 1-2 November 2021, Sana'a Yemen.

Full text available as:

WCLTC_last.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial.


DOI: 10.1109/ITSS-IoE53029.2021.9615301


To 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.

Item Type:Conference or Workshop Item (Paper)
Additional Information:Funding Agency: 10.13039/501100005605-Universiti Malaysia Pahang 10.13039/501100002385-Ministry of Higher Education
Uncontrolled Keywords:Sensitivity , Databases , Transforms , Discrete wavelet transforms; Internet of Things; Task analysis; Viruses (medical)
ID Code:37437
Deposited By: Symplectic RT2
Deposited On:05 Sep 2022 12:19
Last Modified:05 Sep 2022 12:19


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