An Improved Pairwise Rotation Invariant Co-occurrence Local Binary Pattern Method for Texture Feature Extraction

2019 ◽  
Author(s):  
Guangshuai Liu ◽  
Wanghua Liu ◽  
Xiaowen Chen
Keyword(s):  
Feature Extraction ◽  
Local Binary Pattern ◽  
Texture Feature ◽  
Texture Feature Extraction ◽  
Rotation Invariant

Wear Particles Recognition Based on Improved LBP

2013 ◽  
Vol 427-429 ◽  
pp. 1874-1878
Author(s):  
Guo De Wang ◽  
Zhi Sheng Jing ◽  
Guo Wei Qin ◽  
Shan Chao Tu
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Local Binary Pattern ◽  
Recognition Accuracy ◽  
Binary Sequence ◽  
Texture Feature ◽  
Experimental Results ◽  
Wear Particles ◽  
Texture Feature Extraction ◽  
Feature Extraction Method

Wear particles recognition is a key link in the process of Ferrography analysis. Different kinds of wear particles vary greatly in texture, texture feature is one of the most important feature in wear particles recognition. Local Binary Pattern (LBP) is an efficient operator for texture description. The binary sequence of traditional LBP operator is obtained by the comparison between the gray value of the neighborhood and the gray value of the center pixel of the neighborhood, the comparison is too simple to cause the loss of the texture. In this paper, an improved LBP operator is presented for texture feature extraction and it is applied to the recognition of severe sliding particles, fatigue spall particles and laminar particles. The experimental results show that our method is an effective feature extraction method and obtains better recognition accuracy compared with other methods.

Extended Mapping Local Binary Pattern Operator for Texture Classification

2017 ◽  
Vol 31 (06) ◽  
pp. 1750019 ◽  
Author(s):  
Mohammad Hossein Shakoor ◽  
Reza Boostani
Keyword(s):  
Local Binary Pattern ◽  
Accurate Result ◽  
State Of The Art ◽  
Texture Classification ◽  
Texture Feature ◽  
Mapping Technique ◽  
Texture Feature Extraction ◽  
Rotation Invariant ◽  
Classification Rate ◽  
Extended Mapping

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.

scholarly journals Image Classification with Shell Texture Feature Extraction Using Local Binary Pattern (LBP) Method

2020 ◽  
Vol 3 (1) ◽  
pp. 48-57
Author(s):  
Putri Aisyiyah Rakhma Devi ◽  
Rizqi Putri Nourma Budiarti
Keyword(s):  
Feature Extraction ◽  
Local Binary Pattern ◽  
Texture Feature ◽  
Extraction Process ◽  
Input Image ◽  
Texture Feature Extraction ◽  
Classification Procedure ◽  
Svm Classification ◽  
Texture Characteristics

Classification procedure that is usually done manually by way of separation based on the texture of the shell shell. Classification is done by looking at objects based on inherent characteristics usually referred to as features / characteristics. Classification by hand can cause accuracy problems. In the image of the shells, texture characteristics are needed to distinguish one type of shell from another. The purpose of this study is to develop a texture feature extraction system for the classification of shell images. The input image is carried out preprocessing and segmenting to separate objects from the background and the image of the separated object is transformed into a grayscale image for the feature extraction process using the Local Binary Pattern method. Based on trials that have been done, the accuracy is quite good, the highest accuracy value occurs in shellfish blood cockles with RBF kernels. While the lowest accuracy is on testing the feather shell image where the accuracy value is 86.6% this result can show that the LBP method with SVM classification is quite reliable in calculating the accuracy for the classification process of shellfish types.

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