scholarly journals Texture Feature Extraction by Using Local Binary Pattern

Jurnal INKOM ◽  
2016 ◽  
Vol 9 (2) ◽  
pp. 45 ◽  
Author(s):  
Esa Prakasa
Keyword(s):  
Feature Extraction ◽  
Euclidean Distance ◽  
Local Binary Pattern ◽  
Texture Feature ◽  
Texture Feature Extraction ◽  
Distance Method ◽  
Texture Pattern ◽  
Distribution Shape ◽  
Texture Characteristics ◽  
Image Pixels

Local Binary Pattern (LBP) is a method that used to describe texture characteristics of the surfaces. By applying LBP, texture pattern probability can be summarised into a histogram. LBP values need to be determined for all of the image pixels. Texture regularity might be determined based on the distribution shape of the LBP histogram. The implementation results of LBP on two texture types - synthetic and natural textures - shows that extracted texture feature can be used as input for pattern classification. Euclidean distance method is applied to classify the texture pattern obtained from LBPcomputation.

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.

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.

Texture Feature Extraction Using Intuitionistic Fuzzy Local Binary Pattern

2016 ◽  
Vol 29 (1) ◽  
pp. 19-34 ◽  
Author(s):  
Mohd Dilshad Ansari ◽  
Satya Prakash Ghrera ◽  
Arunodaya Raj Mishra
Keyword(s):  
Feature Extraction ◽  
Local Binary Pattern ◽  
Feature Vector ◽  
Texture Feature ◽  
Input Image ◽  
Texture Feature Extraction ◽  
Local Texture ◽  
Intuitionistic Fuzzy ◽  
Pattern Approach ◽  
Local Patterns

Abstract In this paper, intuitionistic fuzzy local binary for texture feature extraction (IFLBP) has been proposed to encode local texture from the input image. The proposed method extends the fuzzy local binary pattern approach by incorporating intuitionistic fuzzy sets in the representation of local patterns of texture in images. Intuitionistic fuzzy local binary pattern also contributes to more than one bin in the distribution of IFLBP values, which can further be used as a feature vector in the various fields of image processing. The performance of the proposed method has been demonstrated on various medical images and processing images of size 256×256. The obtained results validated the effectiveness and usefulness of our proposed method over the other reported methods, and new improvements are suggested.

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