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A Novel Improved local binary pattern and Its Application to the Fault Diagnosis of Diesel Engine

SHOCK AND VIBRATION 2020年第0期2020卷 15-15页

作者： Cai, Yanping Xu, Guanghua Li, Aihua Wang, Xu Xi An Jiao Tong Univ Sch Mech Engn Xian 710049 Peoples R China High Tech Inst Xian Xian 710025 Shaanxi Peoples R China

Aiming at the feature extraction difficulty of vibration signals, an improved local binary pattern- (ILBP-) based diesel engine fault diagnosis approach is proposed. To effectively make use of the component spatial information in time-frequency images, local binary pattern (LBP) algorithm is applied. Also, in view of the problems that traditional LBP coding is easily interfered by singular pixel points and the relative spatial information is not prominent, an improved coding rule of the LBP operator is put forward in this paper. Compared with some typical LBP algorithms, computational complexity of the proposed ILBP algorithm is greatly reduced, and the coding sparsity is greatly improved. The ILBP operator is applied to fault diagnosis of BF4L1011F diesel engine with eight different valve conditions. For comparison, six kinds of time-frequency distribution are used to convert raw vibration signals into time-frequency images, and then circular LBP, rotation-invariant LBP, uniform LBP, and ILBP operator are applied for texture coding. Finally, nearest neighbor classifier (NNC) and support vector machine (SVM) are used for fault identification. The classification results show that the ILBP operator proposed in this paper can better describe the texture feature information in vibration time-frequency images of the diesel engine, and a good diagnostic effect can be achieved by combining wavelet packet (WP) distribution and ILBP.

关键词： local binary pattern

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Variable step dynamic threshold local binary pattern for classification of atrial fibrillation

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2020年 108卷 101932-101932页

作者： Yazid, Muhammad Rahman, Mahrus Abdur Inst Teknol Sepuluh Nopember Dept Biomed Engn Kampus ITS Sukolilo Surabaya 60111 Indonesia Airlangga Univ Fac Med Jl Mayjen Prof Dr Moestopo 47 Surabaya 60132 Indonesia

Objective: In this paper, we proposed new methods for feature extraction in machine learning-based classification of atrial fibrillation from ECG signal. Methods: Our proposed methods improved conventional 1-dimensional local binary pattern method in two ways. First, we proposed a dynamic threshold LBP code generation method for use with 1-dimensional signals, enabling the generated LBP codes to have a more detailed representation of the signal morphological pattern. Second, we introduced a variable step value into the LBP code generation algorithm to better cope with a high sampling frequency input signal without a downsampling process. The proposed methods do not employ computationally expensive processes such as filtering, wavelet transform, up/downsampling, or beat detection, and can be implemented using only simple addition, division, and compare operations. Results: Combining these two approaches, our proposed variable step dynamic threshold local binary pattern method achieved 99.11% sensitivity and 99.29% specificity when used as a feature generation algorithm in support vector machine classification of atrial fibrillation from MIT-BIH Atrial Fibrillation Database dataset. When applied on signals from MIT-BIH Arrhythmia Database, our proposed method achieved similarly good 99.38% sensitivity and 98.97% specificity. Conclusion: Our proposed methods achieved one of the best results among published works in atrial fibrillation classification using the same dataset while using less computationally expensive calculations, without significant performance degradation when applied on signals from multiple databases with different sampling frequencies.

关键词： local binary pattern Dynamic threshold Variable step Atrial fibrillation AFDB MITDB

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local binary pattern-based discriminant graph construction for dimensionality reduction with application to face recognition

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MULTIMEDIA TOOLS AND APPLICATIONS 2019年第16期78卷 22445-22462页

作者： Yang, Bo Li, Qian-zhong Inner Mongolia Univ Coll Comp Sci Hohhot 010021 Peoples R China Sch Phys Sci & Technol Hohhot 010021 Peoples R China Inner Mongolia Key Lab Data Min & Knowledge Engn Hohhot 010021 Peoples R China

Graph construction has attracted increasing interest in recent years due to its key role in many dimensionality reduction (DR) algorithms. On the other hand, our previous study shows that the local-binary-pattern Image (LBPI) representation is a more powerful discriminant and is invariant to monotonic gray level changes. Here, we attempt to construct a discriminant graph for DR in the LBPI representation space. We call the graph the local-binary-Image Discriminant (LBID) graph and further incorporate the LBID graph into the locality Preserving Projection (LPP) to develop an enhanced algorithm - local binary Image Discriminant Preserving Projection (LBIDPP). Meanwhile, we also construct a local-binary-Histogram (LBH) graph in LBP histogram space and obtain the local binary Histogram Preserving Projection (LBHPP) algorithm and compare these to the LBID graph and LBIDPP. It is worth noting that LBIDPP is not a simple combination of the two feature extractions LBP and LPP, i.e., LBP + LPP. LBIDPP inherits the attractive properties of the LBP and LPP. The experiments on face recognition validate the effectiveness and feasibility of the LBID graph and LBIDPP.

关键词： local binary pattern Dimensionality reduction Face recognition Graph embedding

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An improved local binary pattern method for pollen image classification and recognition

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COMPUTERS & ELECTRICAL ENGINEERING 2021年 90卷 106983-106983页

作者： Yin, Huige Chen, Yuantao Xiong, Jie Xia, Runlong Xie, Jingbo Yang, Kai Changsha Univ Sci & Technol Sch Comp & Commun Engn Changsha 410114 Hunan Peoples R China Yangtze Univ Elect & Informat Sch Jingzhou 434023 Hubei Peoples R China Hunan Inst Sci & Tech Informat Changsha 410001 Hunan Peoples R China Hunan ZOOMLION Intelligent Technol Corp Ltd Dept Elect Prod Changsha 410005 Hunan Peoples R China

Most of the current feature extraction algorithms in pollen images perform recognition by extracting the contour, shape, texture, and spatial and frequency domain features of the divided images. For improving the effect of recognition, the improved local binary pattern method is proposed for pollen image classification and recognition. Firstly, the image features are described by calculating and counting the directional histogram of the image's gradient. Secondly, the obtained image normalization processing result determines and generates corresponding samples, and multi-scale features are performed on the pollen images. Finally, the Histogram of Oriented Gradients are fused and binary coded by local binary pattern method, and the similarity of each image is been calculated using Manhattan Distance. The experimental results use the Correct Recognition Rate and the Average Recognition Time. The experimental results can show that the proposed method is superior to other methods and robust to noise and rotation of pollen images.

关键词： Histogram of oriented gradient Generative adversarial networks local binary pattern Manhattan distance Pollen images

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Multiple channels local binary pattern for color texture representation and classification

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SIGNAL PROCESSING-IMAGE COMMUNICATION 2021年 98卷 116392-116392页

作者： Shu, Xin Song, Zhigang Shi, Jinlong Huang, Shucheng Wu, Xiao-Jun Jiangsu Univ Sci & Technol Sch Comp Sci Zhenjiang 212003 Jiangsu Peoples R China Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214122 Jiangsu Peoples R China

Image texture description and analysis technology are the basis of many practical applications in pattern recog-nition. This paper presents a novel and simple, yet powerful method, namely multiple channels local binary pattern (MCLBP), which is the natural extension and development of local binary pattern (LBP) algorithm for color texture representation and classification. MCLBP combines single-channel texture characteristics with multi-channel color information, which reflects the correlations and dependency among different channels. Furthermore, we decompose local color differences into color-difference signs and color-difference magnitudes and MCLBP is extended to MCLBP+M. Then, the resulted image descriptor is a histogram representation, which fuses rich features including color difference sign and color difference magnitude. Comprehensive experiments conducted on five benchmark databases, including Outex, KTH-TIPS, CUReT, STex and KTH-TIPS2-b clearly demonstrate that our proposed method outperforms most of the existed color texture features in terms of classification accuracy. Particularly, our method achieves the best classification performance in CUReT and STex databases.

关键词： Texture descriptor Texture classification Color texture feature local binary pattern Multiple channels local binary pattern

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Intensity prediction of tropical cyclone using multilayer multi-block local binary pattern and tree-based classifiers over North Indian Ocean

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COMPUTERS & GEOSCIENCES 2021年 154卷 104798-104798页

作者： Kar, Chinmoy Banerjee, Sreeparna Sikkim Manipal Inst Technol Dept Comp Sci & Engn Sikkim India Maulana Abul Kalam Azad Univ Technol Dept Nat Sci Kolkata India

local binary pattern (LBP) is an extensively used method in image analysis and pattern recognition related works across various domains. In this paper, we explore cyclone cloud evolution patterns using a modified LBP. The multilayer multi-block local binary pattern (MMLBP) is an extended version of Completed LBP (CLBP) with multiple blocks and many layers. A tropical cyclone (TC) image is converted into 3 x 3 identical blocks to generate central pixels using CLBP magnitude descriptor block-wise (CLBP_MB). These central pixels are used to find the next layer, which is further divided into 3 x 3 blocks to generate central pixels by CLBP_MB descriptor. This technique will iterate until the dimension of the final layer reached to 1 x 1. These central pixels are extracted from each layer and gather in a vector called a feature vector. The proposed method is applied to 600 tropical cyclone (TC) images and each image generates one feature vector. Hence, 600 vectors are passed through tree-based classifiers to classify cyclone images of various classes. The proposed method of feature extraction and classification reaches a maximum of 84.66% accuracy using Random Forest (RF) classifier. The root mean squared error (RMSE) of the MMLBP method is 9.27 kt, which is better than the LBP and MBLBP approaches. Finally, the size of the original feature vector is reduced to 97.4% using correlation based feature subset selection method.

关键词： local binary pattern Tropical cyclone Feature extraction TC intensity Prediction Tree-based classifiers

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Heart sounds classification using convolutional neural network with 1D-local binary pattern and 1D-local ternary pattern features

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APPLIED ACOUSTICS 2021年 180卷 108152-108152页

作者： Er, Mehmet Bilal Harran Univ Fac Engn Dept Comp Engn TR-63300 Sanliurfa Turkey

Cardiovascular diseases are one of those diseases which should be considered to be serious health threats for humans since today it is seen that majority of deaths are caused by heart disorders. There are advanced techniques such as electrocardiograph (ECG), computerized tomography in order to detect heart disorders. However, these methods are usually costly and time-consuming. Besides, auscultating the heart sounds by doctors for the detection of deformation of the heart sounds is an alternative method. Nevertheless, analysis of heart sounds performed by auscultating the patient mostly depends on the practitioner's expertise and skills. Therefore, record and analysis of heart sounds via computers is a preferred method. In this paper, a new heart sound classification model is proposed based on local binary pattern (LBP) and local Ternary (LTP) pattern features and deep learning. In the first step of the model, One Dimensional local binary pattern (1D-LBP) and One Dimensional local Ternary pattern (1D-LTP) are used to extract features from signals. In the second step, the features obtained as a result of the application of 1D-LBP and 1D-LTP are combined and a hybrid feature vector is obtained from a signal. In the third step, ReliefF based feature selection method is used to identify the most relevant features. Finally, the most discriminative features are given as input to the designed One Dimensional Convolutional Neural Network (1D-CNN) in order to complete the classification process. Experiments are made with the help of two popular datasets that are used in the context to determine the efficiency of distinct techniques. These datasets are PASCAL and PhysioNet 2016. We obtain 91.66% and 91.78% accuracy rates for the PASCAL and PhysioNet 2016 datasets respectively. As a consequence, when we compare our results that we obtain from the proposed method in this study with those of the up-to-date methods' results used in the literature, it is obvious that our technique

关键词： Heart sound classification Phonocardiogram local binary pattern local Ternary pattern Deep learning Feature selection

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Refined Color Texture Classification Using CNN and local binary pattern

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MATHEMATICAL PROBLEMS IN ENGINEERING 2021年第1期2021卷

作者： Hosny, Khalid M. Magdy, Taher Lashin, Nabil A. Apostolidis, Kyriakos Papakostas, George A. Zagazig Univ Informat Technol Dept Zagazig 44519 Egypt Sinai Univ Comp Sci Dept North Sinai Arish Egypt Int Hellen Univ Dept Comp Sci MLV Res Grp Kavala 65404 Greece

Representation and classification of color texture generate considerable interest within the field of computer vision. Texture classification is a difficult task that assigns unlabeled images or textures to the correct labeled class. Some key factors such as scaling and viewpoint variations and illumination changes make this task challenging. In this paper, we present a new feature extraction technique for color texture classification and recognition. The presented approach aggregates the features extracted from local binary patterns (LBP) and convolution neural network (CNN) to provide discriminatory information, leading to better texture classification results. Almost all of the CNN model cases classify images based on global features that describe the image as a whole to generalize the entire object. LBP classifies images based on local features that describe the image's key points (image patches). Our analysis shows that using LBP improves the classification task when compared to using CNN only. We test the proposed approach experimentally over three challenging color image datasets (ALOT, CBT, and Outex). The results demonstrated that our approach improved up to 25% in the classification accuracy over the traditional CNN models. We identify optimal combinations for each dataset and obtain high classification rates. The proposed approach is robust, stable, and discriminatory among the three datasets and has shown enhancement in classification and recognition compared to the state-of-the-art method.

关键词： local binary pattern

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Simple Detection of Epilepsy From EEG Signal Using local binary pattern Transition Histogram

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IEEE ACCESS 2021年 9卷 150252-150267页

作者： Yazid, Muhammad Fahmi, Fahmi Sutanto, Erwin Shalannanda, Wervyan Shoalihin, Ruhush Horng, Gwo-Jiun Aripriharta Inst Teknol Sepuluh Nopember Biomed Engn Dept Surabaya 60111 Indonesia Univ Sumatera Utara Fac Engn Dept Elect Engn Medan 20155 Indonesia Univ Airlangga Fac Sci & Technol Dept Phys Surabaya 60115 Indonesia Bandung Inst Technol Sch Elect Engn & Informat Dept Elect Engn Bandung 40116 Indonesia Univ Indonesia Fac Comp Sci Depok 16424 Indonesia Southern Taiwan Univ Sci & Technol Dept Comp Sci & Informat Engn Tainan 71005 Taiwan Univ Negeri Malang Dept Elect Engn Malang 65145 Indonesia

This paper proposed a simple but highly accurate feature extraction method for epilepsy detection from electroencephalogram (EEG) signals. Based on the combination of Discrete Wavelet Transform (DWT) and the newly proposed features local binary pattern Transition Histogram (LBPTH) and local binary pattern Mean Absolute Deviation (LBPMAD), our proposed feature extraction method can efficiently extract features from EEG signals for machine learning classification of epilepsy, achieving high classification accuracy with a feature size of only 18 for each signal. Tested on the publicly available University of Bonn Epilepsy EEG Dataset using a signal length of 4097 data points (23.61 seconds), the proposed method achieved larger than 99.6% accuracy results for Support Vector Machine (SVM) and k-Nearest Neighbor (KNN) classification of ictal (set E) against any non-ictal (set A, B, C, or D) or combinations of non-ictal (set A+B, set C+D, or set A+B+C+D) EEG signals, which is among the best of currently published works. Our method can maintain high classification accuracy even with short input signals, achieving more than 99.1% SVM classification accuracy when input signal length is reduced to 512 data points (2.95 seconds). The high accuracy, small feature size, ability to work with short input signals and low computing requirements made the proposed method suitable for mobile, low power, and low-cost wearable medical devices.

关键词： Feature extraction Electroencephalography Epilepsy Support vector machines Costs Convolutional neural networks Wearable computers Biomedical disability and family support health eeg epilepsy bonn feature extraction local binary pattern machine learning svm knn

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OD-LBP: Orthogonal difference-local binary pattern for Face Recognition

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DIGITAL SIGNAL PROCESSING 2021年 110卷 102948-102948页

作者： Karanwal, Shekhar Diwakar, Manoj Graph Era Deemed Univ Comp Sci & Engn Dept Dehra Dun Uttarakhand India

Most of the local descriptors exists today are based on the relationship between the neighborhood pixels and the center pixel by considering the 3 x3spatial window. Moving one step forward from the previous methodologies used, this research paper introduces the novel descriptor for Face analysis called as orthogonal difference-local binary pattern (OD-LBP). In OD-LBP, initially the 3 gray level differences are computed for each orthogonal position (of 2 groups, in 3 x3pixel window) by subtracting the 2 closest neighborhoods and the center pixel from the respective orthogonal value. Then these respective gray level differences are compared with the value produced after employing the novel comparison method. Finally the 1 x 24 size row vector is produced by concatenating the binary patterns produced from both the orthogonal groups. Eventually the 24 bit row vector is split into three 8 bit binary patterns from which the 3 OD-LBP codes are produced. By employing this concept for each pixel position eventually results in the 3 OD-LBP transformed images. All the 3 transformed images are then divided into 3 x 3 subregions for histogram extraction. The combined sub-regional histograms from all the transformed images are the entire feature size of the OD-LBP descriptor. To achieve the compact feature representation PCA is applied further and then classification is performed by the SVMs. The entire concept is tested on 5 challenging databases which include ORL, GT, JAFFE, MIT-CBCL and Yale. The proposed descriptor (i.e. OD-LBP) achieves very promising results on all 5 databases as it comprehensively outperforms the other 10 state of art descriptors LBP, CS-LBP, 6 x 6 MB-LBP, OC-LBP, MBP, HOG, ELBP, OS-LTP, WCLBP and RLBP on majority of sub-sets. Numerous states of approaches from the literature are also outclassed by proposed descriptor. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Orthogonal difference-local binary pattern local binary pattern Principal component analysis Radial basis function Polynomial function

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