First- and Second-Order Sorted Local Binary Pattern Features for Grayscale-Inversion and Rotation Invariant Texture Classification

In this paper, an Extended Mapping Local Binary Pattern (EMLBP) method is proposed that is used for texture feature extraction. In this method, by extending nonuniform patterns a new mapping technique is suggested that extracts more discriminative features from textures. This new mapping is tested for some LBP operators such as CLBP, LBP, and LTP to improve the classification rate of them. The proposed approach is used for coding nonuniform patterns into more than one feature. The proposed method is rotation invariant and has all the positive points of previous approaches. By concatenating and joining two or more histograms significant improvement can be made for rotation invariant texture classification. The implementation of proposed mapping on Outex, UIUC and CUReT datasets shows that proposed method can improve the rate of classifications. Furthermore, the introduced mapping can increase the performance of any rotation invariant LBP, especially for large neighborhood. The most accurate result of the proposed technique has been obtained for CLBP. It is higher than that of some state-of-the-art LBP versions such as multiresolution CLBP and CLBC, DLBP, VZ_MR8, VZ_Joint, LTP, and LBPV.

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Comparative Analysis of Texture Classification Using Local Binary Pattern and Its Variants

International Journal of Information System Modeling and Design ◽

10.4018/ijismd.2017040103 ◽

2017 ◽

Vol 8 (2) ◽

pp. 45-56

Author(s):

Richa Sharma ◽

Madan Lal

Keyword(s):

Image Processing ◽

Comparative Analysis ◽

Digital Image Processing ◽

High Performance ◽

Local Binary Pattern ◽

Texture Classification ◽

Local Binary Patterns ◽

Classification Performance ◽

Powerful Method ◽

Rotation Invariant

Texture classification is an important issue in digital image processing and the Local Binary pattern (LBP) is a very powerful method used for analysing textures. LBP has gained significant popularity in texture analysis world. However, LBP method is very sensitive to noise and unable to capture the macrostructure information of the image. To address its limitation, some variants of LBP have been defined. In this chapter, the texture classification performance of LBP has been compared with the five latest high-performance LBP variants, like Centre symmetric Local Binary Pattern (CS-LBP), Orthogonal Combination of Local Binary Patterns (OC LBP), Rotation Invariant Local Binary Pattern (RLBP), Dominant Rotated Local Binary Pattern (DRLBP) and Median rotated extended local binary pattern (MRELBP). This was by using the standard images Outex_TC_0010 dataset. From the experimental results it is concluded that DRLBP and MRELBP are the best methods for texture classification.

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Completed Grayscale-Inversion and Rotation Invariant Local Binary Pattern for Texture Classification

2018 24th International Conference on Pattern Recognition (ICPR) ◽

10.1109/icpr.2018.8545438 ◽

2018 ◽

Author(s):

Tiecheng Song ◽

Liangliang Xin ◽

Lin Luo ◽

Chenqiang Gao

Keyword(s):

Local Binary Pattern ◽

Texture Classification ◽

Rotation Invariant

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Completed Local Ternary Pattern for Rotation Invariant Texture Classification

The Scientific World JOURNAL ◽

10.1155/2014/373254 ◽

2014 ◽

Vol 2014 ◽

pp. 1-10 ◽

Cited By ~ 17

Author(s):

Taha H. Rassem ◽

Bee Ee Khoo

Keyword(s):

Classification Accuracy ◽

Local Binary Pattern ◽

Texture Classification ◽

Experimental Results ◽

Rotation Invariant ◽

Local Ternary Pattern ◽

Binary Count ◽

Robust To Noise

Despite the fact that the two texture descriptors, the completed modeling of Local Binary Pattern (CLBP) and the Completed Local Binary Count (CLBC), have achieved a remarkable accuracy for invariant rotation texture classification, they inherit some Local Binary Pattern (LBP) drawbacks. The LBP is sensitive to noise, and different patterns of LBP may be classified into the same class that reduces its discriminating property. Although, the Local Ternary Pattern (LTP) is proposed to be more robust to noise than LBP, however, the latter’s weakness may appear with the LTP as well as with LBP. In this paper, a novel completed modeling of the Local Ternary Pattern (LTP) operator is proposed to overcome both LBP drawbacks, and an associated completed Local Ternary Pattern (CLTP) scheme is developed for rotation invariant texture classification. The experimental results using four different texture databases show that the proposed CLTP achieved an impressive classification accuracy as compared to the CLBP and CLBC descriptors.

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HOLBP: Remote Sensing Image Registration Based on Histogram of Oriented Local Binary Pattern Descriptor

Remote Sensing ◽

10.3390/rs13122328 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2328

Author(s):

Yameng Hong ◽

Chengcai Leng ◽

Xinyue Zhang ◽

Zhao Pei ◽

Irene Cheng ◽

...

Keyword(s):

Image Registration ◽

Local Binary Pattern ◽

Computational Time ◽

Rotation Invariant ◽

Edge Information ◽

Important Research Topic ◽

New Construction ◽

Novel Method ◽

Direction Information ◽

Angle Calculation

Image registration has always been an important research topic. This paper proposes a novel method of constructing descriptors called the histogram of oriented local binary pattern descriptor (HOLBP) for fast and robust matching. There are three new components in our algorithm. First, we redefined the gradient and angle calculation template to make it more sensitive to edge information. Second, we proposed a new construction method of the HOLBP descriptor and improved the traditional local binary pattern (LBP) computation template. Third, the principle of uniform rotation-invariant LBP was applied to add 10-dimensional gradient direction information to form a 138-dimension HOLBP descriptor vector. The experimental results showed that our method is very stable in terms of accuracy and computational time for different test images.

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