Abstract
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.
Highlights
Texture classification is increasingly recognised as a serious issue in the texture analysis field [1]
In the Feat-WITH CLTP (WCLTP) descriptor, first, the redundant discrete wavelet transform RDWT is integrated with the original Completed Local Ternary Pattern (CLTP)
The proposed descriptors are compared with some state-of-art local binary pattern (LBP)-based descriptors, which are the LBP [19], Local Ternary Pattern (LTP) [21], Completed Local Binary Pattern (CLBP) [26], CLBC [27] and CLTP [28]
Summary
Texture classification is increasingly recognised as a serious issue in the texture analysis field [1]. VOLUME 8, 2020 are designed to make better use of the nonuniform patterns instead of dismissing them and provide more noise resistance such as Novel Extended Local Binary Pattern (NELBP) [16] and Noise Tolerant Local Binary Pattern (NTLBP) [24] These descriptors suffer from common serious drawback which is the limited neighbourhood size that leads to high computational complexity in case of generalization to larger scales [25]. Many researchers have tended to focus on enhancing the discriminative power of LBP and improve the rotation invariant texture classification results by combined multiple types of local difference features such as Completed Local Binary Pattern (CLBP) [26] where the feature extraction including the information from the sign, magnitude and centre pixels.
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