Traditional Local Binary Pattern Research Articles

QLBP: Dynamic patterns-based feature extraction functions for automatic detection of mental health and cognitive conditions using EEG signals

BackgroundSevere psychiatric disorders, including depressive disorders, schizophrenia spectrum disorders, and intellectual disability, have devastating impacts on vital life domains such as mental, psychosocial, and cognitive functioning and are correlated with an increased risk of mortality. Accurate symptom monitoring and early diagnosis are essential to optimize treatment and enhance patient outcomes. Electroencephalography (EEG) is a potential diagnostic and monitoring tool for mental health and cognitive disorders, as EEG signals are ideal inputs for machine learning models. In this paper, we propose a novel machine learning model for mental disorder detection based on EEG signals. Aimelectroencephalography (EEG) signals for the detection of three major mental health conditions, namely intellectual disability (ID), schizophrenia (SZ), and bipolar disorder (BD); and (ii) to introduce two novel conditional local binary pattern-based feature extractors for precise classification of these three classes. Material and methodWe collected a novel electroencephalography (EEG) signal dataset from 69 individuals, including a control group and participants diagnosed with bipolar disorder, schizophrenia, and intellectual disability. To extract informative features from the dataset, we developed two novel conditional feature extraction functions that improve upon traditional local binary pattern (LBP) functions by utilizing maximum and minimum distance vectors to generate patterns. We refer to these functions as quantum LBP (QLBP). Additionally, we employed wavelet packet decomposition to construct a multileveled feature extraction model. We evaluated several feature selection techniques, including neighborhood component analysis (NCA), Chi2, maximum relevance minimum redundancy (MRMR), and ReliefF, to select the most informative features. Finally, we employed iterative hard majority voting (IHMV) to obtain the final predicted results. ResultsUsing our multichannel electroencephalography (EEG) signal dataset, we calculated channel-by-channel results and voted results for the classification of intellectual disability (ID), schizophrenia (SZ), and bipolar disorder (BD) classes. Our proposed model, employing the k-nearest neighbors (kNN) classifier with the leave-one subject out cross-validation (LOSO CV) strategy, achieved high accuracy rates of 97.47 %, 94.36 %, and 93.49 % for the ID, SZ, and BD classes, respectively. ConclusionsEmploying the leave-one subject out cross-validation (LOSO CV) technique, our proposed model achieved classification accuracy rates of over 90 % for all cases, thereby providing strong evidence for the effectiveness of the proposed quantum local binary pattern (QLBP) feature extraction method.

Research on Multi-Scale Feature Extraction and Working Condition Classification Algorithm of Lead-Zinc Ore Flotation Foam

To address the problems of difficult online monitoring, low recognition efficiency and the subjectivity of work condition identification in mineral flotation processes, a foam flotation performance state recognition method is developed to improve the issues mentioned above. This method combines multi-dimensional CNN (convolutional neural networks) characteristics and improved LBP (local binary patterns) characteristics. We have divided the foam flotation conditions into six categories. First, the multi-directional and multi-scale selectivity and anisotropy of nonsubsampled shearlet transform (NSST) are used to decompose the flotation foam images at multiple frequency scales, and a multi-channel CNN network is designed to extract static features from the images at different frequencies. Then, the flotation video image sequences are rotated and dynamic features are extracted by the LBP-TOP (local binary patterns from three orthogonal planes), and the CNN-extracted static picture features are fused with the LBP dynamic video features. Finally, classification decisions are made by a PSO-RVFLNs (particle swarm optimization-random vector functional link networks) algorithm to accurately identify the foam flotation performance states. Experimental results show that the detection accuracy of the new method is significantly improved by 4.97% and 6.55%, respectively, compared to the single CNN algorithm and the traditional LBP algorithm, respectively. The accuracy of flotation performance state classification was as high as 95.17%, and the method reduced manual intervention, thus improving production efficiency.

Facial Feature Extraction Using a Symmetric Inline Matrix-LBP Variant for Emotion Recognition.

With a large number of Local Binary Patterns (LBP) variants being currently used today, the significant and importance of visual descriptors in computer vision applications are prominent. This paper presents a novel visual descriptor, i.e., SIM-LBP. It employs a new matrix technique called the Symmetric Inline Matrix generator method, which acts as a new variant of LBP. The key feature that separates our variant from existing counterparts is that our variant is very efficient in extracting facial expression features like eyes, eye brows, nose and mouth in a wide range of lighting conditions. For testing our model, we applied SIM-LBP on the JAFFE dataset to convert all the images to its corresponding SIM-LBP transformed variant. These transformed images are then used to train a Convolution Neural Network (CNN) based deep learning model for facial expressions recognition (FER). Several performance evaluation metrics, i.e., recognition accuracy rate, precision, recall, and F1-score, were used to test mode efficiency in comparison with those using the traditional LBP descriptor and other LBP variants. Our model outperformed in all four matrices with the proposed SIM-LBP transformation on the input images against those of baseline methods. In comparison analysis with the other state-of-the-art methods, it shows the usefulness of the proposed SIM-LBP model. Our proposed SIM-LBP variant transformation can also be applied on facial images to identify a person's mental states and predict mood variations.

Face Recognition Attendance System Method Based on Fusion of LBP and HOG

As one of the hot topics in the field of computer vision research, face recognition technology has received significant attention due to its potentiality for a wide range of applications in government as well as commercial purposes. In practical applications, although several existing face recognition methods have achieved good performances in specific scenes, they easily suffer from a sharp decline in recognition rate if affected by different conditions of light, expression, posture and occlusion. Among many factors, influences of complex illuminations on face recognition are particularly significant. To further improve the performance of the existing local binary pattern (LBP) operator, neighbourhood weighted average LBP (NWALBP) is first proposed for fully considering the strong correlations between pixel pairs in the neighbourhood, which extends the traditional LBP uni-layer neighbourhood template window to the bi-layer neighbourhood template window and calculates the weighted average of bi-layer neighbourhood pixels in each direction. Then, inspired by centre symmetric LBP (CS-LBP), centre symmetric NWALBP (CS-NWALBP) is further proposed, which can effectively reduce computation complexity by only comparing the weighted average values of the neighbourhood pixels that are symmetric about the centre pixel. Finally, by combining the merit of histogram of oriented gradient (HOG), a feature fusion algorithm named CS-NWALBP+HOG is suggested. Several experiments have eventually demonstrated that our proposed algorithms have more robust performance under complex illumination conditions if compared with many other latest algorithms.

Robust baggage detection and classification based on local tri-directional pattern

In recent decades, the automatic video surveillance system has gained significant importance in computer vision community. The crucial objective of surveillance is monitoring and security in public places. In the traditional Local Binary Pattern, the feature description is somehow inaccurate, and the feature size is large enough. Therefore, to overcome these shortcomings, our research proposed a detection algorithm for a human with or without carrying baggage. The Local tri-directional pattern descriptor is exhibited to extract features of different human body parts including head, trunk, and limbs. Then with the help of support vector machine, extracted features are trained and evaluated. Experimental results on INRIA and MSMT17 V1 datasets show that LtriDP outperforms several state-of-the-art feature descriptors and validate its effectiveness.

A Multi-Model Framework for Grading of Human Emotion Using CNN and Computer Vision

Emotion analysis is an area which is been widely used in the forensic crime detection domain, a mentoring device for depressed students, psychologically affected patient treatment. The current system helps only in identifying the emotions but not in identifying the level of emotions like whether the individual is truly happy/sad or pretending to be happy /sad. In this proposed work a novel methodology has been introduced. We have rebuilt the Traditional Local Binary Pattern (LBP) feature operator to image the expression and combine the abstract characteristics of facial expression learned from the neural network of deep convolution with the modified features of the texture of the LBP facial expression in the full connection layer. These extracted features have been subjected as input for CNN Alex Net to classify the level of emotions. The results obtained in this phase are used in the confusion matrix for analysis of grading of emotions like Grade-1, Grade-2, and Grade-3 obtained an accuracy of 87.58% in the comparative analysis.

Modified Local Binary Pattern Algorithm for Feature Dimensionality Reduction

Background: Bio metric authentication is becoming popular now a days and becoming integral part of IoT and other systems. Face recognition is one of the major and important aspect of bio metric systems after the fingerprint. Objective: A face recognition algorithm with feature dimensionality reduction is proposed which is very much required in recognition system for high speed and accuracy. Methods: The proposed algorithm is based on a variant of Local Binary Pattern (LBP) for face detection and recognition. The features of each block of face image is extracted and then global feature of face is constructed from super histogram. Results: For recognition, traditional methods are used. The query image is compared with the data set (ORL Dataset, LFW Dataset and Yale Dataset) in similarity index and the minimum distance. The maximum similarity is used to define as the class of query image. The reduction in number of features is achieved by modifying the traditional LBP process. Conclusion: The proposed modified method is observed as more fast and efficient for face recognition as compared to the existing algorithms.

HOLBP: Remote Sensing Image Registration Based on Histogram of Oriented Local Binary Pattern Descriptor

Image registration has always been an important research topic. This paper proposes a novel method of constructing descriptors called the histogram of oriented local binary pattern descriptor (HOLBP) for fast and robust matching. There are three new components in our algorithm. First, we redefined the gradient and angle calculation template to make it more sensitive to edge information. Second, we proposed a new construction method of the HOLBP descriptor and improved the traditional local binary pattern (LBP) computation template. Third, the principle of uniform rotation-invariant LBP was applied to add 10-dimensional gradient direction information to form a 138-dimension HOLBP descriptor vector. The experimental results showed that our method is very stable in terms of accuracy and computational time for different test images.

Face anti-spoofing based on weighted neighborhood pixel difference pattern

The vulnerability of face recognition-based unlocking systems to spoofing attacks is a serious problem. With the development of science and technology, face pictures on the Internet are particularly easy to obtain and recreate. A face spoofing attack happens when someone fakes a face and tries to pass the verification of the face recognition system, which seriously threatens the security of users’ information. We propose a method based on quantifying the difference between neighborhood pixels around the center of a local region, namely, weighted neighborhood difference quantization local binary pattern (LBP), against print attacks, and video-replay attacks. The proposed method quantifies the differences between neighborhood pixels without using the center one and uses a linear weighting scheme to improve the discriminant capability of the traditional LBP. The combination of the proposed algorithm and the spatial pyramid further improves the performance of face spoofing detection. We also conducted a lot of experiments in different color spaces to illustrate the role of color in face spoofing detection. The improved method achieves better results in three challenging face anti-spoofing databases, CASIA FASD, Replay-Attack, and Replay-Mobile, respectively.

Local Neighbourhood Edge Responsive Image Descriptor for Texture Classification Using Gaussian Mutated JAYA Optimization Algorithm

Local feature descriptor plays a significant role in texture classification. However, in the traditional local binary pattern method, image pixels are converted into a binary pattern based on the relationship between center and neighborhood pixels. This paper introduces a novel feature extraction method named local neighbourhood edge responsive binary pattern to extract and categorize reliable texture features from images for achieving an automated classification of different types of ocean bottom sediments. Initially, the basic magnitude variance pixel values are derived depending on an odd and even pixel value of a 3 × 3 image patch. Further, the edge information is extracted using the local directional pattern method from all images. The edge response of the image is obtained using a kirsch mask in all eight directions. The encoding condition is then applied to both the local intensity and the edge information to create a unique descriptor value. Finally, a new learning algorithm called GMJAYA-ELM combines the Gaussian mutated JAYA (GMJAYA) with an extreme learning machine (ELM) for texture classification. The GMJAYA is used to optimize the input weights and hidden biases of single-hidden-layer feed-forward neural networks. Experimental findings indicate that the suggested method yields better accuracy and sensitivity efficiency among various groups. The proposed algorithm is tested by contrasting outcomes with standard learning schemes such as PSO-ELM, GA-ELM, ABC-ELM, Birdswarm-ELM, and JAYA-ELM, suggesting the dominance of GMJAYA-ELM.

Asphalt pavement paving segregation detection method using more efficiency and quality texture features extract algorithm

The segregation of asphalt pavement is the main reason for the decrease of safety, comfort and actual service life of the road, and the paving segregation is the main inducement for asphalt pavements segregation. Thus, a kind of effective paving segregation detection method can reduce the occurrence of asphalt pavement segregation. The traditional asphalt segregation detection methods are mainly divided into contact detection and non-contact detection. The contact detection method can only detect the segregation of pavement after paving or in use, and the non-contact detection method is also generally limited by the noise and expensive equipment. In recent years, the rapid development of image processing technology has provided a new research direction for asphalt paving segregation detection, but the accuracy and efficiency of the existing image-based asphalt paving segregation detection methods are insufficient. In order to solve these problems, this paper proposes an asphalt paving segregation detection method based on image texture features. Firstly, based on the traditional algorithms LBP (Local Binary Pattern) and GLCM (Gray-level Co-occurrence Matrix), a new texture feature extraction algorithm uniform pattern LBP-GLCM is proposed. Secondly, a detection method based on uniform pattern LBP-GLCM in combination with SVM (Support Vector Machine) is proposed. Then, the detection method proposed is validated using Kylbery texture dataset, the result show that this detection methods has great accuracy and efficiency in the classification of targets with similar texture features, it also means the texture feature extract method based on uniform pattern LBP-GLCM can combine the advantages of LBP and GLCM to achieve improvement of feature extraction's performance and efficiency. Finally, the detection method is applied to the diagnosis of asphalt paving segregation, and the accuracy of diagnosis achieves 94%. Compared with the existing algorithms, detection method based on uniform pattern LBP-GLCM has higher diagnostic accuracy and efficiency. Specifically, detection method with uniform pattern LBP-GLCM can improve accuracy in comparison with single asphalt pavement paving segregation detection method, and it can improve efficiency in comparison with existing hybrid asphalt pavement paving segregation detection method. The results of this study can potentially be used for real-time detection of asphalt paving segregation.

Development of a Robust Multi-Scale Featured Local Binary Pattern for Improved Facial Expression Recognition.

Compelling facial expression recognition (FER) processes have been utilized in very successful fields like computer vision, robotics, artificial intelligence, and dynamic texture recognition. However, the FER’s critical problem with traditional local binary pattern (LBP) is the loss of neighboring pixels related to different scales that can affect the texture of facial images. To overcome such limitations, this study describes a new extended LBP method to extract feature vectors from images, detecting each image from facial expressions. The proposed method is based on the bitwise AND operation of two rotational kernels applied on LBP(8,1) and LBP(8,2) and utilizes two accessible datasets. Firstly, the facial parts are detected and the essential components of a face are observed, such as eyes, nose, and lips. The portion of the face is then cropped to reduce the dimensions and an unsharp masking kernel is applied to sharpen the image. The filtered images then go through the feature extraction method and wait for the classification process. Four machine learning classifiers were used to verify the proposed method. This study shows that the proposed multi-scale featured local binary pattern (MSFLBP), together with Support Vector Machine (SVM), outperformed the recent LBP-based state-of-the-art approaches resulting in an accuracy of 99.12% for the Extended Cohn–Kanade (CK+) dataset and 89.08% for the Karolinska Directed Emotional Faces (KDEF) dataset.

New local binary pattern approaches based on color channels in texture classification

In this paper, four novel, simple and robust approaches, which are left to right local binary patterns (LBPLL2R), top to down local binary patterns (LBPT2D), cube surface local binary pattern (LBPSurfaces), and cube diagonal local binary pattern (LBPDiagonal), were proposed in order to exact texture features in color images. These approaches were based on the local binary pattern (LBP), which is an effective statistical texture descriptor and can be employed in gray images. Proposed approaches were evaluated and validated in four datasets, which are Outex, KTH_TIPS, KTH_TIPS2, and USPtex datasets. The images in these datasets are in RGB, HSV, YIQ, and YCbCr color formats. Achieved results by these approaches were compared with the obtained results by the classical LBP and literature findings. As a result, the proposed approaches performed better than the traditional LBP method and they found effective in the classification of color texture images, especially in images, which are in RGB and HSV formats. Furthermore, noise robustness and time complexity of the proposed approaches were validated.

Detection of coronavirus Disease (COVID-19) based on Deep Features and Support Vector Machine

The detection of coronavirus (COVID-19) is now a critical task for the medical practitioner. The coronavirus spread so quickly between people and approaches 100,000 people worldwide. In this consequence, it is very much essential to identify the infected people so that prevention of spread can be taken. In this paper, the deep feature plus support vector machine (SVM) based methodology is suggested for detection of coronavirus infected patient using X-ray images. For classification, SVM is used instead of deep learning based classifier, as the later one need a large dataset for training and validation. The deep features from the fully connected layer of CNN model are extracted and fed to SVM for classification purpose. The SVM classifies the corona affected X-ray images from others. The methodology consists of three categories of Xray images, i.e., COVID-19, pneumonia and normal. The method is beneficial for the medical practitioner to classify among the COVID-19 patient, pneumonia patient and healthy people. SVM is evaluated for detection of COVID-19 using the deep features of different 13 number of CNN models. The SVM produced the best results using the deep feature of ResNet50. The classification model, i.e. ResNet50 plus SVM achieved accuracy, sensitivity, FPR and F1 score of 95.33%,95.33%,2.33% and 95.34% respectively for detection of COVID-19 (ignoring SARS, MERS and ARDS). Again, the highest accuracy achieved by ResNet50 plus SVM is 98.66%. The result is based on the Xray images available in the repository of GitHub and Kaggle. As the data set is in hundreds, the classification based on SVM is more robust compared to the transfer learning approach. Also, a comparison analysis of other traditional classification method is carried out. The traditional methods are local binary patterns (LBP) plus SVM, histogram of oriented gradients (HOG) plus SVM and Gray Level Co-occurrence Matrix (GLCM) plus SVM. In traditional image classification method, LBP plus SVM achieved 93.4% of accuracy.

JL-GFDN: A Novel Gabor Filter-Based Deep Network Using Joint Spectral-Spatial Local Binary Pattern for Hyperspectral Image Classification

The traditional local binary pattern (LBP, hereinafter we also call it a two-dimensional local binary pattern 2D-LBP) is unable to depict the spectral characteristics of a hyperspectral image (HSI). To cure this deficiency, this paper develops a joint spectral-spatial 2D-LBP feature (J2D-LBP) by averaging three different 2D-LBP features in a three-dimensional hyperspectral data cube. Subsequently, J2D-LBP is added into the Gabor filter-based deep network (GFDN), and then a novel classification method JL-GFDN is proposed. Different from the original GFDN framework, JL-GFDN further fuses the spectral and spatial features together for HSI classification. Three real data sets are adopted to evaluate the effectiveness of JL-GFDN, and the experimental results verify that (i) JL-GFDN has a better classification accuracy than the original GFDN; (ii) J2D-LBP is more effective in HSI classification in comparison with the traditional 2D-LBP.

Visual saliency based global–local feature representation for skin cancer classification

With the rapid increase in the cases of deadly skin cancer, the classification on different types of skin cancer has been emerging as one of the most significant issues in the field of medical image. Several approaches have been proposed to help in diagnosing the categories of the skin lesions by means of traditional features or leveraging the widely used deep learning models. However, there are lack of the integrated frameworks to combine the hand-crafted traditional features and the deep Conv-features. Furthermore, the effective way to extract global and local features is also conducive to distinguish the specific lesions from normal skin. Hence, in this study, the authors present an integrated model to acquire more representative global–local features including the traditional local binary pattern features and deep Conv-features. In addition, several fusion strategies have conducted on the Global-DNN and Local-DNN for better performance. In order to extract more explicit features from the specific lesion areas, a target segmentation method based on visual saliency detection is employed to eliminate the background interference. Experimental results on ISIC-2017 skin cancer dataset demonstrate that the proposed Global-DNN and Global-Local models can obtain more effective feature representation which achieve outperformed results for skin cancer classification.

Human Emotion Recognition Based on Face and Facial Expression Detection Using Deep Belief Network Under Complicated Backgrounds

The human emotion recognition based on facial expression has a significant meaning in the application of intelligent man–machine interaction. However, the human face images vary largely in real environments due to the complex backgrounds and luminance. To solve this problem, this paper proposes a robust face detection method based on skin color enhancement model and a facial expression recognition algorithm with block principal component analysis (PCA). First, the luminance range of human face image is broadened and the contrast ratio of skin color is strengthened by the homomorphic filter. Second, the skin color enhancement model is established using YCbCr color space components to locate the face area. Third, the feature based on differential horizontal integral projection is extracted from the face. Finally, the block PCA with deep neural network is used to accomplish the facial expression recognition. The experimental results indicate that in the case of weaker illumination and more complicated backgrounds, both the face detection and facial expression recognition can be achieved effectively by the proposed algorithm, meanwhile the mean recognition rate obtained by the facial expression recognition method is improved by 2.7% comparing with the traditional Local Binary Patterns (LBPs) method.

Feature-Level Fusion of Finger Vein and Fingerprint Based on a Single Finger Image: The Use of Incompletely Closed Near-Infrared Equipment

Due to its portability, convenience, and low cost, incompletely closed near-infrared (ICNIR) imaging equipment (mixed light reflection imaging) is used for ultra thin sensor modules and have good application prospects. However, equipment with incompletely closed structure also brings some problems. Some finger vein images are not clear and there are sparse or even missing veins, which results in poor recognition performance. For these poor quality ICNIR images, however, there is additional fingerprint information in the image. The analysis of ICNIR images reveals that the fingerprint and finger vein in a single ICNIR image can be enhanced and separated. We propose a feature-level fusion recognition algorithm using a single ICNIR finger image. Firstly, we propose contrast limited adaptive histogram equalization (CLAHE) and grayscale normalization to enhance fingerprint and finger vein texture, respectively. Then we propose an adaptive radius local binary pattern (ADLBP) feature combined with uniform pattern to extract the features of fingerprint and finger vein. It solves the problem that traditional local binary pattern (LBP) is unable to describe the texture features of different sizes in ICNIR images. Finally, we fuse the feature vectors of ADLBP block histogram for a fingerprint and finger vein, and realize feature-layer fusion recognition by a threshold decision support vector machine (T-SVM). The experimentation results showed that the performance of the proposed algorithm was noticeably better than that of the single model recognition algorithm.

A Novel Improved Local Binary Pattern and Its Application to the Fault Diagnosis of Diesel Engine

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.

A Face Recognition System using Directional Binary Code Algorithm and Multi-SVM

Over past few years, face recognition technology plays an important function in the development of biometric identifier with less time consuming and computational overhead. Many researchers were put their effort to develop face recognition algorithm involves three distinct steps such as detection, unique faceprint creation and finally verification. Traditional Local binary pattern based face recognition system slow down the recognition speed, high computational complexity and does not give the directional data of the picture. In order to overcome the above limitation, a novel face recognition system is proposed by employing the advantage of Directional Binary Code (DBC) feature extraction method. The face images features are extracted from DBC are generally smoother than other feature extraction methods. The images with blur creation, pose changes, and illumination is applied and stored in the database. For blur creation various filters such as Average filter, Gaussian filter and Motion filter are used. By using Directional Binary Code method, the face is detected and extracted. Then the same algorithm is used for input images and with help of Multi-SVM classifier multiple images in the database is compared and shows the matched images. Finally, simulation result shows the implemented results in term of its recognition speed and computation complexity.