Local Binary Pattern Feature Extraction Research Articles

Pengenalan Logo Kendaraan Menggunakan Metode Local Binary Pattern dan Random Forest

The vehicle logo is one of the features that can be used to identify a vehicle. Even so, a lot of Intelligent Transport System which are developed nowadays has yet to use a vehicle logo recognition system as one of its vehicle identification tools. Hence there are still cases of traffic crimes that haven't been able to be examined by the system, such as cases of counterfeiting vehicle license plates. Vehicle logo recognition itself could be done by using various feature extraction and classification methods. This research project uses the Local Binary Pattern feature extraction method which is often used for many kinds of image recognition systems. Then, the classification method used is Random Forest which is known to be effective and accurate for various classification problems. The data used for this study were as many as 2000 vehicle logo images consisting of 5 brand classes, namely Honda, Kia, Mazda, Mitsubishi, and Toyota. The results of the tests carried out obtained the best accuracy value of 88.89% for the front view logo image dataset, 77.03% for the side view logo image dataset, and 83% for the dataset with both types of images.

Analysis of Random Local Descriptors in Face Recognition

This article describes and analyzes the new feature extraction technique, Random Local Descriptor (RLD), that is used for the Permutation Coding Neural Classifier (PCNC), and compares it with Local Binary Pattern (LBP-based) feature extraction. The paper presents a model of face feature detection using local descriptors, and describes an improvement on the PCNC for the recognition of plane rotated and small displaced face images, as applied to three databases, i.e., ORL, FRAV3D and FEI. All databases are described along with the recognition results that were obtained. We also include a comparison of our classifier with the Support Vector Machine (SVM) and Iterative Closest Point (ICP). The ORL database was selected to compare our RLDs with LBP-based algorithms. The PCNC with the RLDs demonstrated the best recognition rate, i.e., 97.49%, in comparison with 90.49% for LBPs. For the FEI image database, we obtained the best recognition rate, i.e., 93.57%, in comparison with 66.74% for LBPs. Using the RLDs and rotating the original images for FRAV3D, we improved the recognition rate by decreasing by approximately twice the number of errors. In addition, we analyzed the influence of different RLD parameters on the quality of facial recognition.

Research on the application of local binary patterns based on color distance in image classification

Local Binary Patterns (LBP) is an texture feature, which is widely used in texture discrimination, face recognition, painting classification and other fields. The previous LBP feature extraction methods and many improved algorithms are all based on the gray scale image. Because there is information loss during the conversion from color image to gray image, the LBP methods need combine with other color features to improve the accuracy of classification. In this paper, a LBP encoding method for color image based on color space distance is proposed, which can not only directly achieve LBP feature from color image, but also can be applied to improve various previous methods. The experiment shows that color-based LBP method can filter background information better. With three different classifiers, the accuracy of classification which only used color-based LBP features is about 15% higher than that of existing LBP features. Finally, the application effect in local binary patterns histograms (LBPH) with color-based LBP further proves the advantage.

Ensemble Learner for Covid-19 from Lung X-Ray Images

Despite Reverse Transcription-Polymerase Chain Reaction (RT-PCR) is the gold standard of Covid-19 detection, some underdeveloped countries are lacking financially and suffer underdeveloped health system to perform fast Covid-19 detection. Both RT-PCR and Computed Tomography (CT) scan are costly diagnosis tool, thus computed diagnostic chest x-ray (CXR) is seen as fast and affordable option to perform Covid-19 diagnosis for underdeveloped countries. Despite of other works suggest to perform Local binary Pattern (LBP) and recent feature extraction methods such as Local Phase Quantization (LPQ), this works employed Gray-Level Co-Occurrence Matrix (GLCM) because it is a powerful method to extract textured features from gray-level images of chest x-ray. The learner to classify Covid-19 detection is tested via non tree-based learner such as k-Nearest Neighbour (kNN). This work also compared the performance especially in the tree-based and voting approach classifier. The experimentation shows that tree-based which uses voting and ensemble approach to detect Covid-19 from CXR images is a possible candidate learner to be improved for the underdeveloped countries.

Klasifikasi Pneumonia pada Chest X-Ray Paru-paru dengan Ekstraksi Fitur Local Binary Pattern Menggunakan Support Vector Machine

Pneumonia is a type of lung disease caused by bacteria, viruses, fungi, or parasites. One way to find out pneumonia is by X-ray or x-ray. X-ray results will be analyzed by experts in the field to find out whether there is pneumonia or not. This study aims to classify the x-ray results whether there is pneumonia or normal. The method used for the classification of Support Vector Machine (SVM) and Local Binary Pattern (LBP) feature extraction. Stages carried out in the image before the classification are Cropping and Resize, then feature extraction using Local Binary Pattern and in classification using Support Vector Machine produces the best accuracy of 65.63%.
 
 Keywords : Pneumonia, Chest X-Ray, LBP, SVM

Monitoring Urban Deprived Areas with Remote Sensing and Machine Learning in Case of Disaster Recovery

Rapid urbanization and increasing population in cities with a large portion of them settled in deprived neighborhoods, mostly defined as slum areas, have escalated inequality and vulnerability to natural disasters. As a result, monitoring such areas is essential to provide information and support decision-makers and urban planners, especially in case of disaster recovery. Here, we developed an approach to monitor the urban deprived areas over a four-year period after super Typhoon Haiyan, which struck Tacloban city, in the Philippines, in 2013, using high-resolution satellite images and machine learning methods. A Support Vector Machine classification method supported by a local binary patterns feature extraction model was initially performed to detect slum areas in the pre-disaster, just after/event, and post-disaster images. Afterward, a dense conditional random fields model was employed to produce the final slum areas maps. The developed method detected slum areas with accuracies over 83%. We produced the damage and recovery maps based on change analysis over the detected slum areas. The results revealed that most of the slum areas were reconstructed 4 years after Typhoon Haiyan, and thus, the city returned to the pre-existing vulnerability level.

Evaluation of texture feature based on basic local binary pattern for wood defect classification

Wood defects detection has been studied a lot recently to detect the defects on the wood surface and assist the manufacturers in having a clear wood to be used to produce a high-quality product. Therefore, the defects on the wood affect and reduce the quality of wood. This research proposes an effective feature extraction technique called the local binary pattern (LBP) with a common classifier called Support Vector Machine (SVM). Our goal is to classify the natural defects on the wood surface. First, preprocessing was applied to convert the RGB images into grayscale images. Then, the research applied the LBP feature extraction technique with eight neighbors (P=8) and several radius (R) values. After that, we apply the SVM classifier for the classification and measure the proposed technique's performance. The experimental result shows that the average accuracy achieved is 65% on the balanced dataset with P=8 and R=1. It indicates that the proposed technique works moderately well to classify wood defects. This study will consequently contribute to the overall wood defect detection framework, which generally benefits the automated inspection of the wood defects.

Performance Evaluation of Face Recognition system by Concatenation of Spatial and Transformation Domain Features

Face biometric system is one of the successful applications of image processing. Person recognition using face is the challenging task since it involves identifying the 3D object from 2D object. The feature extraction plays a very important role in face recognition. Extraction of features both in spatial as well as frequency domain has more advantages than the features obtained from single domain alone. The proposed work achieves spatial domain feature extraction using Asymmetric Region Local Binary Pattern (ARLBP) and frequency domain feature extraction using Fast Discrete Curvelet Transform (FDCT). The obtained features are fused by concatenation and compared with trained set of features using different distance metrics and Support Vector Machine (SVM) classifier. The experiment is conducted for different face databases. It is shown that the proposed work yields 95.48% accuracy for FERET, 92.18% for L-space k, 76.55% for JAFFE and 81.44% for NIR database using SVM classifier. The results show that the proposed system provides better recognition rate for SVM classifier when compare to the other distance matrices. Further, the work is also compared with existing work for performance evaluation.

Prediction of polyps and bleeding by CS-LBP using RF and SVM

In this paper, a method is proposed to differentiate the classes such as Bleeding and Polyps between the many classes present in GI tract using efficient classification model. The challenge in all image analysis is to characterize these images which improve the exact detection of these abnormalities. To solve this problem, a Center-Symmetric Local Binary Pattern (CS-LBP) feature extraction with Random Forest (RF) and Support Vector Machine (SVM) methods are employed to classify these two classes. Our model shows an excellent detection accuracy of 90.39 for polyps and 94.85for bleeding respectively for the CS-LBP with RF and SVM.

Multi-Classifier Decision-Level Fusion Classification of Workpiece Surface Defects Based on a Convolutional Neural Network

Various defects are formed on the workpiece surface during the production process. Workpiece surface defects are classified according to various characteristics, which includes a bumped surface, scratched surface and pit surface. Suppliers analyze the cause of workpiece surface defects through the defect types and thus determines the subsequent processing. Therefore, the correct classification is essential regarding workpiece surface defects. In this paper, a multi-classifier decision-level fusion classification model for workpiece surface defects based on a convolutional neural network (CNN) was proposed. In the proposed model, the histogram of oriented gradient (HOG) was used to extract the features of the second fully connected layer of the CNN, and the features of the HOG were further extracted by using the local binary patterns (LBP), which was called the HOG–LBP feature extraction. Finally, this paper designed a symmetry ensemble classifier, which was used to classify the features of the last fully connected layer of the CNN and the features of the HOG–LBP. The comprehensive decision was made by fusing the classification results of the symmetry structure channels. The experiments were carried out, and the results showed that the proposed model could improve the accuracy of the workpiece surface defect classification.

Application of soft computing techniques in machine reading of Quranic Kufic manuscripts

Research work in the field of offline Arabic handwriting recognition has seen an exponential growth during past decades. Despite much work dedicated to the recognition of handwritten Arabic text, there remains a lot to achieve in this regard. Though there exists a lot of work that is confined to the Arabic text, this paper presents an approach towards classifying and recognizing text written in one of the famous scripts of Arabic language i.e. Kufic script. The approach based on character segmentation does not perform well in recognizing Kufic text due to various complexities. The proposed system is based on word segmentation and employs a Histogram of Oriented Gradient (HOG) and Local Binary Pattern (LBP) feature extraction techniques. Later in the paper a comparison is given between the numerical results of proposed technique with previous Arabic text recognition techniques to show the effectiveness of present work. This approach yields effective results with 97.05% accuracy in recognizing the Arabic text written in Kufic script using Polynomial kernel of SVM classifier. Experimental results show that the proposed system for recognition of Kufic script performs better than the previous recognition systems for Arabic text.

Brain tumor classification using modified local binary patterns (LBP) feature extraction methods

Automatic classification of brain tumor types is very important for accelerating the treatment process, planning and increasing the patient’s survival rate. Today, MR images are used to determine the type of brain tumor. Manual diagnosis of brain tumor type depends on the experience and sensitivity of radiologists. Therefore, researchers have developed many brain tumor classification models to minimize the human factor. In this study, two different feature extraction (nLBP and αLBP) approaches were used to classify the most common brain tumor types; Glioma, Meningioma, and Pituitary brain tumors. nLBP is formed based on the relationship for each pixel around the neighbors. The nLBP method has a d parameter that specifies the distance between consecutive neighbors for comparison. Different patterns are obtained for different d parameter values. The αLBP operator calculates the value of each pixel based on an angle value. The angle values used for calculation are 0, 45, 90 and 135. To test the proposed methods, it was applied to images obtained from the brain tumor database collected from Nanfang Hospital, Guangzhou, China, and Tianjin Medical University General Hospital between the years of 2005 and 2010. The classification process was performed by using K-Nearest Neighbor (Knn) and Artificial Neural Networks (ANN), Random Forest (RF), A1DE, Linear Discriminant Analysis (LDA) classification methods, with the feature matrices obtained with nLBP, αLBP and classical LBP from the images in the data set. The highest success rate in brain tumor classification was 95.56% with the nLBPd = 1 feature extraction method and Knn model.

An Ensemble Classifier to Predict Protein-Protein Interactions by Combining PSSM-based Evolutionary Information with Local Binary Pattern Model.

Protein plays a critical role in the regulation of biological cell functions. Among them, whether proteins interact with each other has become a fundamental problem, because proteins usually perform their functions by interacting with other proteins. Although a large amount of protein–protein interactions (PPIs) data has been produced by high-throughput biotechnology, the disadvantage of biological experimental technique is time-consuming and costly. Thus, computational methods for predicting protein interactions have become a research hot spot. In this research, we propose an efficient computational method that combines Rotation Forest (RF) classifier with Local Binary Pattern (LBP) feature extraction method to predict PPIs from the perspective of Position-Specific Scoring Matrix (PSSM). The proposed method has achieved superior performance in predicting Yeast, Human, and H. pylori datasets with average accuracies of 92.12%, 96.21%, and 86.59%, respectively. In addition, we also evaluated the performance of the proposed method on the four independent datasets of C. elegans, H. pylori, H. sapiens, and M. musculus datasets. These obtained experimental results fully prove that our model has good feasibility and robustness in predicting PPIs.

Automatic voice based disease detection method using one dimensional local binary pattern feature extraction network

Voices have been widely used for disease detection in the literature but these methods are non-invasive. In this article a 1D local binary pattern (LBP) based feature extraction network (1D-LBPNet) is proposed to extract stable features from voices. The proposed 1D-LBPNet is inspired by convolutional neural networks (CNN) for instance AlexNet, GoogleNet, ResNet. Then, a voice based disease recognition method is presented in this paper. The presented voice based disease recognition method consists of feature extraction using 1D-LBPNet, feature concatenation, feature reduction using neighborhood component analysis (NCA) and classification phases. In the feature extraction phase, 1D-LBPNet extracts 256 × 8 = 2048 features because it has 7 layers. The extracted features are concatenated in the feature concatenation phase. To reduce the concatenated features, a NCA based feature reduction method is used. 1 nearest neighbor (1NN) classifier is utilized as classifier to demonstrate distinctive of the extracted features. To test performance of the proposed method, Saarbruecken Voice Database (SVD) is used in this article. /a/ vowels of the Cordectomy and frontolateral resection diseases are chosen to test the proposed 1D-LBPNet based recognition method. 10 cases are defined using single and concatenated voices for each disease. The results and comparisons clearly shown that the proposed 1D-LBPNet achieved high success rates and these results clearly proved success of the proposed method.

Self-Adapting Patch Strategies for Face Recognition

In this paper, we propose two self-adapting patch strategies, which are obtained by employing the integral projection technique on images’ edge images, while the edge images are recovered by the two-dimensional discrete wavelet transform. The patch strategies are equipped with the advantage of considering the single image’s unique properties and maintaining the integrity of some particular local information. Combining the self-adapting patch strategies with local binary pattern feature extraction and the classifier of the forward and backward greedy algorithms under strong sparse constraint, we propose two new face recognition methods. Experiments are run on the Georgia Tech, LFW and AR face databases. The obtained numerical results show that the new methods outperform some related patch-based methods to a larger extent.

Local Ternary Cross Structure Pattern: A Color LBP Feature Extraction with Applications in CBIR

With the advent of medical endoscopes, earth observation satellites and personal phones, content-based image retrieval (CBIR) has attracted considerable attention, triggered by its wide applications, e.g., medical image analytics, remote sensing, and person re-identification. However, constructing effective feature extraction is still recognized as a challenging problem. To tackle this problem, we first propose the five-level color quantizer (FLCQ) to acquire a color quantization map (CQM). Secondly, according to the anatomical structure of the human visual system, the color quantization map (CQM) is amalgamated with a local binary pattern (LBP) map to construct a local ternary cross structure pattern (LTCSP). Third, the LTCSP is further converted into the uniform local ternary cross structure pattern (LTCSPuni) and the rotation-invariant local ternary cross structure pattern (LTCSPri) in order to cut down the computational cost and improve the robustness, respectively. Finally, through quantitative and qualitative evaluations on face, objects, landmark, textural and natural scene datasets, the experimental results illustrate that the proposed descriptors are effective, robust and practical in terms of CBIR application. In addition, the computational complexity is further evaluated to produce an in-depth analysis.

SSL: A Novel Image Hashing Technique Using SIFT Keypoints with Saliency Detection and LBP Feature Extraction against Combinatorial Manipulations

Image hashing schemes have been widely used in content authentication, image retrieval, and digital forensic. In this paper, a novel image hashing algorithm (SSL) by incorporating the most stable keypoints and local region features is proposed, which is robust against various content-preserving manipulations, even multiple combinatorial manipulations. The proposed algorithm combines S_cale invariant feature transform (SIFT) with S_aliency detection to extract the most stable keypoints. Then, the L_ocal binary pattern (LBP) feature extraction method is exploited to generate local region features based on these keypoints. After that, the information of keypoints and local region features are merged into a hash vector. Finally, a secret key is used to randomize the hash vector, which can prevent attackers from forging the image and the hash value. Experimental results demonstrate that the proposed hashing algorithm can identify visually similar images which are under both single and combinatorial content-preserving manipulations, even multiple combinations of manipulations. It can also identify maliciously forged images which are under various content-changing manipulations. The collision probability between hashes of different images is nearly zero. Besides, the evaluation of key-dependent security shows that the proposed scheme is secure that an attacker cannot forge or estimate the correct hash value without the knowledge of the secret key.

A Palm Vein Recognition System based on a Support Vector Machine

Palm vein authentication is among the recent research in the field of access control applications. Because palm veins are tough to forge, they act as a reliable metric in security applications. Accurate extraction of palm veins is challenging in the presence of various dynamics, such as variant light conditions, variations in palm vein patterns from person to person, the cleanliness of the hand, etc. This paper presents a robust recognition process that makes use of a ridge filter for vein pattern extraction, and local binary patterns (LBP) for feature extraction. The ridge filter takes the major eigenvalue of the Hessian matrix, which contains the second-order derivative of the image pixels. The eigenvalue is then processed using LBP feature extraction from the vein patterns. Finally, a support vector machine is used for classification of subsequent images. The result shows that the system can provide accuracy of 89%, with a computation time of 0.423s. The false acceptance rate and false rejection rate were also evaluated as benchmark parameters, which show significantly good performance.

Research on Feature Extraction of Tumor Image Based on Convolutional Neural Network

Medical images play a very important role in making the right diagnosis for the doctor and in the patient’s treatment process. Using intelligent algorithms makes it possible to quickly distinguish the lesions of medical images, and it is especially important to extract features from images. Many studies have integrated various algorithms into medical images. For medical image feature extraction, a large amount of data is analyzed to obtain processing results, helping doctors to make more accurate case diagnosis. In view of this, this paper takes tumor images as the research object, and first performs local binary pattern feature extraction of the tumor image by rotation invariance. As the image shifts and the rotation changes, the image is stationary relative to the coordinate system. The method can accurately describe the texture features of the shallow layer of the tumor image, thereby enhancing the robustness of the image region description. Focusing on image feature extraction based on convolutional neural network (CNN), the basic framework of CNN is built. In order to break the limitations of machine vision and human vision, the research is extended to multi-channel input CNN for image feature extraction. Two convolution models of Xception and Dense Net are built to improve the accuracy of the CNN algorithm. It can be seen from the experimental results that the CNN algorithm shows high accuracy in tumor image feature extraction. In this paper, the CNN algorithm is compared with several classical algorithms in the local binary mode. The CNN algorithm has more accurate feature extraction ability for tumor CT images on a larger data basis. Furthermore, the advantages of CNN algorithms in this field are demonstrated.

Entropy based Local Binary Pattern (ELBP) feature extraction technique of multimodal biometrics as defence mechanism for cloud storage

Cloud Computing (CC) is a technology that is growing by leaps and bounds and has attracted wide spectrum of users. The extensive usage of cloud technology is influenced by multiple factors like ease of use, pay-per usage strategy, easy access, cost-effectiveness etc. Though it is a widely used technology, challenges exist in the form of security threats. There are a variety of services that are offered by cloud. These include Software as a Service (SaaS), Infrastructure as a Service (IaaS) and Platform as a Service (PaaS). Storage is one of the key service offerings under IaaS. To provide a secure digital platform for users to work with, this research work proposes a novel security architecture for secured storage in cloud that provides a robust authentication by employing multiple biometric modalities from users and allow/deny access accordingly. The crux of better authentication relies on the way the features are extracted from multiple biometric sensors and matched with registered users. For this purpose, a novel feature extraction technique is proposed in this research work. Entropy Based Local Binary Pattern (ELBP) is a new texture-based feature extraction technique proposed to describe the entropy information into Local Binary Pattern histogram in one-dimensional space. ELBP feature extraction technique needs no quantization. Biometric images exhibit higher uniqueness and hence incorporating entropy values into local regions add higher information content to these images, thus leading to better feature extraction. The experiments are performed on biometric images from Chinese Academy of Science, Institute of Automation (CASIA) Iris, Face and Fingerprint databases and the results show that the proposed ELBP feature extraction achieves substantial improvement, in terms of various classification metrics like accuracy, precision, recall etc. in comparison with the conventional rotation invariant LBP methods. The Receiver Operating Characteristics Curve (ROC) also bears testimony to the performance of the authentication system.