Scene image classification using a wigner-based Local Binary Patterns descriptor

ECG beat type analysis is important in the detection of various heart diseases. The ECG beats give useful information about the status of the monitored heart condition. Up to now, various artificial intelligence-based methods have been proposed for ECG based heart failure detection. These methods were generally based on either time or frequency domain signal processing routines. In this study, we propose a different approach for ECG beat classification. The proposed approach is based on image processing. Thus, the initial step of the proposed work is converting the ECG beat signals to the ECG beat images. To do that, the ECG beat snapshots are initially saved as ECG beat images and then local feature descriptors are considered for feature extraction from ECG beat images. Eight local feature descriptors namely Local Binary Patterns, Frequency Decoded LBP, Quaternionic Local Ranking Binary Pattern, Binary Gabor Pattern, Local Phase Quantization, Binarized Statistical Image Features, CENsus TRansform hISTogram and Pyramid Histogram of Oriented Gradients are considered for feature extraction. The Support Vector Machines (SVM) classifier is used in the classification stage of the study. Linear, Quadratic, Cubic and Gaussian kernel functions are used in the SVM classifier. Five types of ECG beats from the MIT-BIH arrhythmia dataset are considered in experiments and the classification accuracy is used for performance measure. To construct a balanced training and test sets, 5000 and 10,000 ECG beat samples are randomly selected and are used in experiments in tenfold cross-validation fashion. The obtained results show that the proposed method is quite efficient where the calculated accuracy score is 99.9% and the comparisons with the state-of-the-art method show that the proposed method outperforms other methods.

The texture is an essential property of an image that tells about the arrangements of pixels of different intensities in an image matrix. Researchers have used many descriptors for texture classification of the images in computer vision and image processing. Local Binary Pattern (LBP) is one of the descriptors, which is computationally efficient and straightforward for texture classification. In recent years, researchers have made many enhancements to the basic LBP method to improve the quality of extracted features for an image. This paper presents a Local Roughness Binary Pattern (LRBP) descriptor for feature extraction of an image to increase LBP discriminative capability. This new descriptor extracts features from the local region of an image called a partial feature vector and concatenates these local features to obtain a feature vector of an image. This feature vector is used as input to a classifier to classify the class of an image. We tested the proposed descriptor for the images available on CUReT and KTHTIPS2b databases with Support Vector Machine (SVM) and k-nearest Neighbors (KNN) classifiers. We have presented the experimental results obtained from the analysis process regarding classification accuracy and confusion matrix. Finally, we have presented a performance comparison between the proposed method, basic LBP descriptor, and its variants. Results show that the proposed descriptor gives much better results than other descriptors on the KTHTIPS2b Database when we use the KNN classifier.KeywordsLBPLocal binary patternLRBPLocal roughness binary patternSVMSupport vector machineKNN K-nearest neighbors

This paper proposes a novel face representation approach, local Gabor binary mapping pattern (LGBMP), for multi-view gender classification. In this approach, a face image is first represented as a series of Gabor magnitude pictures (GMP) by applying multi-scale and multi-orientation Gabor filters. Each GMP is then encoded as a LGBP image where a uniform local binary pattern (LBP) operator is used. After that, each LGBP image is divided into non-overlapping rectangular regions, from which spatial histograms are extracted. Although an LGBP feature vector can be obtained by fitting together the regional histograms, it can not be employed in pattern classification due to its high dimension. We propose that each regional LGBP feature be mapped onto a one-dimensional subspace independently before they are concatenated as a whole feature vector. This is attractive since we reduce the feature dimension and also preserve the spatial information of LGBP image. Two ways have been proposed to map the regional LGBP feature in this paper. One is so-called LGBMP-LDA using linear discriminant analysis (LDA) for dimensionality reduction while the other is to project the regional LGBP feature onto the class center connecting line, namely, LGBMP-CCL. As a result, despite several decades of Gabor filters, the final feature dimension is even less than that of the feature extracted by using LBP directly on gray-scale images. The classification tasks in our work are performed by support vector machines (SVM). The experimental results on the CAS-PEAL face database indicate that the proposed approach achieves higher accuracy than the others such as SVMs+Gray-scale pixel, SVMs+Gabor and SVMs+LBP approach, more particularly, it has the lowest dimension of feature vector.

This research has developed an effective multi-class classification model for images from streaming platforms. Texture features were extracted from the images and used with machine learning algorithms. Two datasets were employed: ’mosaics’ comprising 153,488 images across 14 classes and ’descriptors’ containing 33,471 images across 11 classes. All images had a resolution of 1280 × 720 pixels. The local binary pattern (LBP) technique encoded the local texture structure into 59-element feature vectors for each image. Ten algorithms were trained and evaluated on these vectors for each dataset, including support vector machines (SVM) with linear, polynomial, and Gaussian kernels at various scales, as well as ensemble methods like boosted trees, bagging, discriminant analysis, and k-nearest neighbors in subspaces. Training and validation were done via 30 random splits to mitigate bias. Performance metrics like accuracy, sensitivity, specificity, precision, and F1-score were computed per class. The SVM classifier achieved top mean performance: 0.998952 accuracy, 0.992528 sensitivity, 0.999438 specificity, 0.988132 precision, and 0.990280 F1-score. The results validate the proposed LBP feature extraction and machine learning methodology for effectively classifying images across streaming platforms.

Communication is the ultimate of man’s search for conveying his ideas, emotions, and concepts. Dance is one of the media of communication through which dancers share notion of feelings, with the spectators through gestures, i.e., mudra. Gesture recognition propagates a concept without verbal speech or listening, and in dance recognition, the notion is transferred through various dance poses and actions. This activity in a way really paves way to enhance Indian Sign Language. This study focuses to solve the mudra resemblance in Bharatanatyam through a new system developed with image processing and classification technique using histogram of oriented gradient (HOG) feature extraction techniques and support vector machine (SVM) classifier. SVM classifies the features of HOG into mudras as text labels. Popular feature vectors such as scale-invariant feature transform (SIFT), speed up robust feature (SURF), and local binary pattern (LBP) are hardened against HOG for accuracy and speediness, and this innovative proposed concept is useful for online dance learners.

Local detail features of face are important bases for recognizing different persons. For its invariant to monotonic gray-scale transformations and it's a non-parametric kernel which summarizes the local special structure of an image, the Local Binary Pattern (LBP) has becoming a popular technique for face representation. In this paper, the LBP technique and its application for representing faces are investigated. Two experiments using the histogram sequence of block LBP are performed on ORL face database to validate the effectiveness. In experiment 1, Carle square dissimilarity measure is used as a classifier directly, to classification the histogram sequences of LBP, which have been extracted in partitioned face images. In experiment 2, Principal Component Analysis (PCA) method is used to classification the histogram sequences, which have been converted to vectors. The results show that the number of blocks partitioned in face image is related to the recognition rate. Too much and too little are not beneficial to recognition. In addition, two typical methods are performed on ORL face database to compare the performance of LBP method with others. And the experimental results show that method used in experiment 2 obtains a better classification performance compared with experiment 1 and two typical methods.

A variety of binary feature descriptors such as local binary pattern (LBP) and its variations have recently attracted considerable attention for modelling backgrounds, due to their robustness and strong discriminatory power. However, most existing binary feature descriptors fail to model complex scenes due to their sensitivity to noise. In this paper, we propose an effective local compact binary descriptor for background modelling. For each image, local compact binary patterns (LCBPs) are first extracted by computing a number of low-dimensional pixel difference vectors (PDVs). Then, the LCBP is extended to the spatiotemporal domain taking into account the temporal persistence of pixels, and a novel local compact binary descriptor, STLCBP, is proposed. Multiple color spaces are also considered in order to separate foreground from background pixels accurately. Finally, a joint domain-range adaptive kernel density estimate (KDE) model is used to estimate the background and foreground scores by combining texture features with color features. Experimental results on two well-known datasets, I2R and CDnet2014, demonstrate that the proposed approach significantly outperforms many state-of-the-art methods and works effectively on a wide range of complex videos.

Copy-move forgery is a type of image tampering that involves copying a portion of an image and pasting it to another part of the same image with the intention of deceiving the viewer. In recent years, many approaches have been proposed to detect copy-move forgery, including those based on local binary patterns (LBP). In this paper, we perform a comprehensive evaluation of LBP-based methods for copy-move forgery detection using a dataset of 50 digital images. We compare the performance of four LBP-based methods, namely LBP, SIFT and SURF using metrics such as accuracy, precision, recall, and F1-score. Our results show that LBP outperforms the other methods in terms of accuracy and F1-score, while SIFT has the highest precision and recall. We also investigate the effect of various parameters, such as patch size and threshold values, on the performance of LBP. Our study provides valuable insights into the strengths and weaknesses of LBP-based methods for copy-move forgery detection, which can guide future research in this area. This study evaluates the performance of Local Binary Patterns (LBP) for detecting copy-move forgery in digital images. LBP is a widely used feature extraction technique in image processing and has been applied to various computer vision tasks, including forgery detection. The comparative study involves analyzing the accuracy, precision, recall, and F1-score of LBP and other popular forgery detection techniques, including SIFT and SURF, using a dataset of 50 digital images. The results show that LBP performs better than the other techniques, achieving an accuracy of 96.6%, precision of 94.0%, recall of 100%, and F1-score of 96.9%. This study provides useful insights for researchers and practitioners in the field of forgery detection, particularly for those interested in using LBP as a feature extraction technique.

Depression is a common mental disease characterized by significant sadness and feeling blue all the time. At present, most classifications and predictions of depression rely on different characteristics. Comparing with the previous work, we use local binary pattern (LBP) and signal singular spectrum analysis (SSA) technology to extract features from the original signal. Firstly, the LBP signal is obtained by encoding the segmented signal. Then, we use SSA to decompose and reconstruct the LBP signal to remove noise and divide the frequency band. Finally, we feed the data of each frequency band to K-nearest neighbor (KNN), decision tree (DT), support vector machine (SVM) and extreme learning machine (ELM) for classification. The experimental results show that LBP and SSA features achieve the best classification effect on SVM, and the accuracy of beta band is the highest with 99.24% accuracy, 99.34% sensitivity and 99.12% specificity respectively.

Breast density is considered to be one of the major risk factors in developing breast cancer. High breast density can also affect the accuracy of mammographic abnormality detection due to the breast tissue characteristics and patterns. We reviewed variants of local binary pattern descriptors to classify breast tissue which are widely used as texture descriptors for local feature extraction. In our study, we compared the classification results for the variants of local binary patterns such as classic LBP (Local Binary Pattern), ELBP (Elliptical Local Binary Pattern), Uniform ELBP, LDP (Local Directional Pattern) and M-ELBP (Mean-ELBP). A wider comparison with alternative texture analysis techniques was studied to investigate the potential of LBP variants in density classification. In addition, we investigated the effect on classification when using descriptors for the fibroglandular disk region and the whole breast region. We also studied the effect of the Region-of-Interest (ROI) size and location, the descriptor size, and the choice of classifier. The classification results were evaluated based on the MIAS database using a ten-run ten-fold cross validation approach. The experimental results showed that the Elliptical Local Binary Pattern descriptors and Local Directional Patterns extracted most relevant features for mammographic tissue classification indicating the relevance of directional filters. Similarly, the study showed that classification of features from ROIs of the fibroglandular disk region performed better than classification based on the whole breast region.

This work has the objective of developing a methodology for the classification of regions of interest (ROI) extracted from mammograms into masses and non-masses. To this end, we conducted a comparative study by combining several techniques in the various stages of the problem. We compared several texture-based techniques for the classification of ROIs. This study combines image processing, phylogenetic trees (PT), local binary patterns (LBP), support vector machines (SVM) and the micro-genetic algorithm (GA). The analysis of texture is performed, either through the combination PT/LBP or with grey levels, to compute the taxonomic diversity () and taxonomic distinction (*) indexes extracted from sub-regions (circular, circular crown, internal mask, external mask and the combination of internal and external mask) of an ROI. A GA is used to estimate the best phylogenetic weights. Then, its results are compared with the results achieved with the use of PT only. We also analyse the behaviour of the methodology when using the ROIs with and without enhancement. This enhancement consists of the application of a mean filter and the contrast-limited adaptive histogram equalisation (CLAHE). PTs and SVM were used to perform the selection of features. To evaluate the performance of the methodologies under analysis, we used the following metrics: sensitivity, specificity, accuracy and area under the receiver operating characteristic (ROC) curve (). Sensitivity and specificity measure the efficiency of the classifier at the correct detection of positive (masses) and negative (non-masses) cases, respectively. Accuracy measures the performance of the classification in both cases. The ROC curve is the graphical representation of the pairs (1-specificity, sensitivity). is the area formed by the ROC curve, which equals to 1 in an ideal test. The comparison of the possible combinations for each stage of the study revealed the following results. In the analyses without feature selection, the best results were (1) 100% accuracy and of 0.99 for the combination of PT, LBP and internal masks and (2) 99.5% accuracy and of 0.99 for the combination of GA, LBP and internal masks for the extraction of features. In the analyses with feature selection, the best results were (1) 100% accuracy and of 1.0 for the combination of GA and LBP (feature extraction) with the union of internal and external masks and (2) 98.5% accuracy and of 0.99 for the combination of PT, LBP and the union of internal and external masks.

A case study on computer-aided diagnosis of nonerosive reflux disease using deep learning techniques

This paper proposes a network classification framework based on multiresolution local binary pattern techniques and convolutional neuronal network models. Local binary pattern is used as a descriptor in order to evaluate the data in the spatial domain of the hyperspectral data. The convolutional neural networks offer powerful feature extraction capabilities over the spectral information defining the high-resolution data sets. Our proposed model is based on one dimensional convolutional network (1D-CNN) applied over the histograms obtained from the rotational invariant local binary pattern technique. With respect to other concurrent methods applied on textured hyperspectral data bases, the novel proposed method has similar results with other LBP-ID-CNN classification networks, but has smaller number of parameters and reduced time for training and testing. The experimental results demonstrate that the proposed method can improve the accuracy on Indian Pines and Salinas data sets even with a limited number of training samples.

Palm tree detection using remote sensing images has received increasing attention in recent years, concerning the issues of sustainability, productivity and profitability. There has been significant progress in the research of using machine learning techniques, especially convolutional neural networks (CNNs) for automatic palm tree detection. However, whether CNNs can actually outperform traditional human-engineered approaches in terms of classification accuracy and detection speed is yet unknown. In the present study, we have compared human-engineered features namely histogram of oriented gradients (HOG), local binary pattern (LBP) and scale-invariant feature transform (SIFT) with features extracted using pre-trained AlexNet model for detecting palm trees in high resolution images obtained via unmanned aerial vehicle (UAV). Support vector machines (SVM) with linear and non-linear kernels were used to classify feature vectors obtained by different feature extractors. Haar-like features used in Viola-Jones framework was also tested in the study. Results showed that features extracted from the fifth convolutional layer of AlexNet achieved the highest accuracy of 96.1% using SVM with RBF kernel as classifier, which surpassed the accuracy obtained by fully-connected CNN, namely 95.6%. The results also suggest that SVM classifier with radial basis function (RBF) kernel using LBP as features is the optimal combination as it achieved comparable accuracy but higher detection speed than CNN approaches.

Human action recognition from 2D videos is a demanding area due to its broad applications. Many methods have been proposed by the researchers for recognizing human actions. The improved accuracy in identifying human actions is desirable. This paper presents an improved method of human action recognition using support vector machine (SVM) classifier. This paper proposes a novel feature descriptor constructed by fusing the various investigated features. The handcrafted features such as scale invariant feature transform (SIFT) features, speed up robust features (SURF), histogram of oriented gradient (HOG) features and local binary pattern (LBP) features are obtained on online 2D action videos. The proposed method is tested on different action datasets having both static and dynamically varying backgrounds. The proposed method achieves shows best recognition rates on both static and dynamically varying backgrounds. The datasets considered for the experimentation are KTH, Weizmann, UCF101, UCF sports actions, MSR action and HMDB51.The performance of the proposed feature fusion model with SVM classifier is compared with the individual features with SVM. The fusion method showed best results. The efficiency of the classifier is also tested by comparing with the other state of the art classifiers such as k-nearest neighbors (KNN), artificial neural network (ANN) and Adaboost classifier. The method achieved an average of 94.41% recognition rate.