A new feature-based wavelet completed local ternary pattern (Feat-WCLTP) for texture image classification

Shamaileh, Abeer and Rassem, Taha H. and Siau Chuin, Liew and Al Sayaydeh, Osama Nayel (2020) A new feature-based wavelet completed local ternary pattern (Feat-WCLTP) for texture image classification. IEEE Access, 8. 28276 -28288. ISSN 2169-3536. (Published)

[img] Pdf
A New Feature-Based Wavelet Completed.pdf
Restricted to Repository staff only

Download (3MB) | Request a copy
A New Feature-Based Wavelet Completed Local Ternary Pattern.pdf

Download (199kB) | Preview


LBP is one of the simplest yet most powerful feature extraction descriptors. Many descriptors based on LBP have been proposed to improve its performance. Completed Local Ternary Pattern (CLTP) is one of the important LBP variants that was proposed to overcome LBP's drawbacks. However, despite the impressive performance of CLTP, it suffers from some limitations, such as high dimensionality, thereby leading to higher computation time and may affect the classification accuracy. In this paper, a new rotation invariant texture descriptor (Feat-WCLTP) is proposed. In the proposed Feat-WCLTP descriptor, first the redundant discrete wavelet transform RDWT is integrated with the original CLTP. Then, CLTP is extracted based on the LL wavelet coefficients. Next, the mean and variance features are used to describe the magnitude information instead of using P-dimensional features as the normal magnitude components of CLTP. Reducing the number of extracted features positively affected the computational complexity of the descriptor and the dimensionality of the resultant histogram. The proposed Feat-WCLTP is evaluated using four texture datasets and compared with some well-known descriptors. The experimental results show that Feat-WCLTP outperformed the other descriptors in terms of classification accuracy. It achieves 99.66% in OuTex, 96.89% in CUReT, 95.23% in UIUC and 99.92% in the Kylberg dataset. The experimental results showed that the Feat-WCLTP not only overcomes the CLTP's dimensionality problem but also further improves the classification accuracy.

Item Type: Article
Additional Information: Indexed by Scopus & WOS
Uncontrolled Keywords: Texture classification; Local binary pattern (LBP); Completed local ternary pattern (CLTP), RDWT
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Faculty/Division: Faculty of Computing
Depositing User: Dr. Taha Hussein Alaaldeen Rassem
Date Deposited: 14 Jul 2020 02:01
Last Modified: 14 Jul 2020 02:01
URI: http://umpir.ump.edu.my/id/eprint/28453
Download Statistic: View Download Statistics

Actions (login required)

View Item View Item