Local binary patterns partitioning for rotation invariant texture classification

2012 ◽  
Author(s):  
Navid Shadkam ◽  
Mohammad Sadegh Helfroush ◽  
Kamran Kazemi
Keyword(s):  
Texture Classification ◽  
Local Binary Patterns ◽  
Rotation Invariant

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

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.

Global and Local Oriented Edge Magnitude Patterns for Texture Classification

2017 ◽  
Vol 31 (03) ◽  
pp. 1750007 ◽  
Author(s):  
Jun Dong ◽  
Xue Yuan ◽  
Fanlun Xiong
Keyword(s):  
Texture Classification ◽  
Local Binary Patterns ◽  
Texture Descriptor ◽  
Oriented Edge ◽  
Rotation Invariant ◽  
Edge Information ◽  
Structure Information ◽  
Local Texture ◽  
Rotation Compensation ◽  
Global And Local

In this paper, we propose a gray-scale texture descriptor, name the global and local oriented edge magnitude patterns (GLOEMP), for texture classification. GLOEMP is a framework, which is able to effectively combine local texture, global structure information and contrast of texture images. In GLOEMP, the principal orientation is determined by Histogram of Gradient (HOG) feature, then each direction is respectively shown in detail by a local binary patterns (LBP) occurrence histogram. Due to the fact that GLOEMP characterizes image information across different directions, it contains very abundant information. The global-level rotation compensation method is proposed, which shifts the principal orientation of the HOG to the first position, thus allowing GLOEMP to be robust to rotations. In addition, gradient magnitudes are used as weights to add to the histogram, making GLOEMP robust to lighting variances as well, and it also possesses a strong ability to express edge information. The experimental results obtained from the representative databases demonstrate that the proposed GLOEMP framework is capable of achieving significant improvement, in some cases reaching classification accuracy of 10% higher than over the traditional rotation invariant LBP method.

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