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.

An ensemble feature method for food classification

2017 ◽  
Vol 26 (1) ◽  
pp. 13-39
Author(s):  
Niki Martinel ◽  
Christian Micheloni ◽  
Claudio Piciarelli
Keyword(s):  
Random Forest ◽  
State Of The Art ◽  
Texture Feature ◽  
Random Forest Classifier ◽  
Texture Feature Extraction ◽  
Feature Encoding ◽  
Food Recognition ◽  
Feature Based ◽  
Food Image ◽  
Comparative Results

In the last years, several works on automatic image-based food recognition have been proposed, often based on texture feature extraction and classification. However, there is still a lack of proper comparisons to evaluate which approaches are better suited for this specific task. In this work, we adopt a Random Forest classifier to measure the performances of different texture filter banks and feature encoding techniques on three different food image datasets. Comparative results are given to show the performance of each considered approach, as well as to compare the proposed Random Forest classifiers with other feature-based state-of-the-art solutions.

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.

HEp-2 Cell Classification Using Descriptors Fused into the Dissimilarity Space into the Dissimilarity Space

2014 ◽  
Vol 23 (03) ◽  
pp. 1460006
Author(s):  
Ilias Theodorakopoulos ◽  
Dimitris Kastaniotis ◽  
George Economou ◽  
Spiros Fotopoulos
Keyword(s):  
Autoimmune Diseases ◽  
Classification Scheme ◽  
State Of The Art ◽  
Svm Classifier ◽  
Cell Level ◽  
Rotation Invariant ◽  
Classification Rate ◽  
Stage Classification ◽  
Linear Svm ◽  
Serum Detection

Autoimmune diseases are strictly connected with the presence of autoantibodies in patient serum. Detection of Antinucleolar Antibodies (ANAs) in patient serum is performed using a laboratory technique named Indirect Immunofluorescence (IIF) followed by manual evaluation on the acquired slides from specialized personnel. In this procedure, several limitations appear and several automatic techniques have been proposed for the task of ANA detection. In this work we present a method achieving state-of-the-art performance on a publicly available dataset. More precisely, two powerful and rotation invariant descriptors are incorporated into a two stage classification scheme where the feature vectors are represented and fused in the dissimilarity space. Then, in a second level dissimilarity vectors are classified using a linear SVM classifier. Evaluation on the HEp-2 cell contest dataset yields a 70.16% performance on cell-level classification. Furthermore we provide results in Image Level Classification where a 78.57% classification rate was achieved.

Feature based local binary pattern for rotation invariant texture classification

2017 ◽  
Vol 88 ◽  
pp. 238-248 ◽  
Author(s):  
Zhibin Pan ◽  
Zhengyi Li ◽  
Hongcheng Fan ◽  
Xiuquan Wu
Keyword(s):  
Local Binary Pattern ◽  
Texture Classification ◽  
Rotation Invariant ◽  
Feature Based

Principal curvatures based local binary pattern for rotation invariant texture classification

Optik ◽  
2019 ◽  
Vol 193 ◽  
pp. 162999 ◽  
Author(s):  
Qiqi Kou ◽  
Deqiang Cheng ◽  
Liangliang Chen ◽  
Yinping Zhuang
Keyword(s):  
Local Binary Pattern ◽  
Texture Classification ◽  
Principal Curvatures ◽  
Rotation Invariant

Texture Segmentation by Genetic Programming

2008 ◽  
Vol 16 (4) ◽  
pp. 461-481 ◽  
Author(s):  
Andy Song ◽  
Vic Ciesielski
Keyword(s):  
Feature Extraction ◽  
Genetic Programming ◽  
Texture Classification ◽  
Texture Feature ◽  
Texture Features ◽  
Texture Segmentation ◽  
Segmentation Method ◽  
Texture Feature Extraction ◽  
Powerful Approach ◽  
Computation Algorithms

This paper describes a texture segmentation method using genetic programming (GP), which is one of the most powerful evolutionary computation algorithms. By choosing an appropriate representation texture, classifiers can be evolved without computing texture features. Due to the absence of time-consuming feature extraction, the evolved classifiers enable the development of the proposed texture segmentation algorithm. This GP based method can achieve a segmentation speed that is significantly higher than that of conventional methods. This method does not require a human expert to manually construct models for texture feature extraction. In an analysis of the evolved classifiers, it can be seen that these GP classifiers are not arbitrary. Certain textural regularities are captured by these classifiers to discriminate different textures. GP has been shown in this study as a feasible and a powerful approach for texture classification and segmentation, which are generally considered as complex vision tasks.

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