Classification of Depression Based on Local Binary Pattern and Singular Spectrum Analysis

This paper proposes a face recognition algorithm based on the combination of local binary pattern (LBP) texture features and extreme learning machine (ELM). The face image is divided into several regions, and the LBP features are extracted from these regions and combined together to form a feature vector which will be the input data of ELM. It shows that ELM performs well in classification applications, and ELM and support vector machine (SVM) are equivalent from the optimization point of view. But ELM has milder optimization constraints and much less training time. Our experiments are carried out on two well-known face databases, and the results show that compared with compared to PCA+NN, PCA+SVM and PCA+ELM the proposed method can achieve higher recognition rates.

This study compares the performance of local binary patterns (LBP) and scale invariant feature transform (SIFT) with support vector machines (SVM) in automatic classification of discrete facial expressions. Facial expression recognition is a multiclass classification problem and seven classes; happiness, anger, sadness, disgust, surprise, fear and comtempt are classified. Using SIFT feature vectors and linear SVM, 93.1% mean accuracy is acquired on CK+ database. On the other hand, the performance of LBP-based classifier with linear SVM is reported on SFEW using strictly person independent (SPI) protocol. Seven-class mean accuracy on SFEW is 59.76%. Experiments on both databases showed that LBP features can be used in a fairly descriptive way if a good localization of facial points and partitioning strategy are followed.

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

We investigated the feasibility of using multiresolution Local Binary Pattern (LBP) texture analysis to reduce falsepositive (FP) detection in a computerized mass detection framework. A new and novel approach for extracting LBP features is devised to differentiate masses and normal breast tissue on mammograms. In particular, to characterize the LBP texture patterns of the boundaries of masses, as well as to preserve the spatial structure pattern of the masses, two individual LBP texture patterns are then extracted from the core region and the ribbon region of pixels of the respective ROI regions, respectively. These two texture patterns are combined to produce the so-called multiresolution LBP feature of a given ROI. The proposed LBP texture analysis of the information in mass core region and its margin has clearly proven to be significant and is not sensitive to the precise location of the boundaries of masses. In this study, 89 mammograms were collected from the public MAIS database (DB). To perform a more realistic assessment of FP reduction process, the LBP texture analysis was applied directly to a total of 1,693 regions of interest (ROIs) automatically segmented by computer algorithm. Support Vector Machine (SVM) was applied for the classification of mass ROIs from ROIs containing normal tissue. Receiver Operating Characteristic (ROC) analysis was conducted to evaluate the classification accuracy and its improvement using multiresolution LBP features. With multiresolution LBP features, the classifier achieved an average area under the ROC curve, , z A of 0.956 during testing. In addition, the proposed LBP features outperform other state-of-the-arts features designed for false positive reduction.

Gender identification is a new domain in image recognition. Gender identification of human face is to judge one's gender according to his/her face features. The article adopted local binary pattern (LBP) algorithm to build feature subspaces, and processed data using Support Vector Machine (SVM) learning models. Experiments showed that integration of LBP algorithm with linear SVM and integration of LBP algorithm with low-order polynomial SVM are better than Gauss-kernel function.

The principle objective of this chapter is to build up a churn prediction model which helps telecom administrators to foresee clients who are no doubt liable to agitate. Many studies affirmed that AI innovation is profoundly effective to anticipate this circumstance as it is applied through training from past information. The prediction procedure is involved three primary stages: normalization of the data, then feature selection based on information gain, and finally, classification utilizing different AI methods, for example, back propagation neural network (BPNNM), naïve Bayesian, k-nearest neighborhood (KNN), support vector machine (SVM), discriminant analysis (DA), decision tree (DT), and extreme learning machine (ELM). It is shown from simulation study that out of these seven methods SVM with polynomial based kernel is coming about 91.33% of precision where ELM is at the primary situation with 92.10% of exactness and MLANN-based CCP model is at third rank with 90.4% of accuracy. Similar observation is noted for 10-fold cross validation also.

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.

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.

In the field of quantitative microscopy, textural information plays a significant role very often in tissue characterization and diagnosis, in addition to morphology and intensity. The objective of this work is to improve the classification accuracy based on textural features for the development of a computer assisted screening of oral sub-mucous fibrosis (OSF). In fact, the approach introduced is used to grade the histopathological tissue sections into normal, OSF without dysplasia (OSFWD) and OSF with dysplasia (OSFD), which would help the oral onco-pathologists to screen the subjects rapidly. The main objective of this work is to evaluate the use of Higher Order Spectra (HOS) features and Local Binary Pattern (LBP) features extracted from the epithelial layer in classifying normal, OSFWD and OSFD. For this purpose, we extracted twenty three HOS features and nine LBP features and fed them to a Support Vector Machine (SVM) for automated diagnosis. One hundred and fifty eight images (90 normal, 42 OSFWD and 26 OSFD images) were used for analysis. LBP features provide a good sensitivity of 82.85% and specificity of 87.84%, and the HOS features provide higher values of sensitivity (94.07%) and specificity (93.33%) using SVM classifier. The proposed system, can be used as an adjunct tool by the onco-pathologists to cross-check their diagnosis.

Since reporting cases of breast cancer are on the rise all over the world. Especially in regions such as Pakistan, Saudi Arabia, and the United States. Efficient methods for the early detection and diagnosis of breast cancer are needed. The usual diagnosis procedures followed by physicians has been updated with modern diagnostic approaches that include computer-aided support for better accuracy. Machine learning based practices has increased the accuracy and efficiency of medical diagnosis, which has helped save lives of many patients. There is much research in the field of medical imaging diagnostics that can be applied to the variety of data such as magnetic resonance images (MRIs), mammograms, X-rays, ultrasounds, and histopathological images, but magnetic resonance (MR) and mammogram imaging have proved to present the promising results. The proposed paper has presented the results of classification algorithms over Breast Cancer (BC) mammograms from a novel dataset taken from hospitals in the Qassim health cluster of Saudi Arabia. This paper has developed a novel approach called the novel spectral extraction algorithm (NSEA) that uses feature extraction and fusion by using local binary pattern (LBP) and bilateral algorithms, as well as a support vector machine (SVM) as a classifier. The NSEA with the SVM classifier demonstrated a promising accuracy of 94% and an elapsed time of 0.68 milliseconds, which were significantly better results than those of comparative experiments from classifiers named Naïve Bayes, logistic regression, K-Nearest Neighbor (KNN), Gaussian Discriminant Analysis (GDA), AdaBoost and Extreme Learning Machine (ELM). ELM produced the comparative accuracy of 94% however has a lower elapsed time of 1.35 as compared to SVM. Adaboost has produced a fairly well accuracy of 82%, KNN has a low accuracy of 66%. However Logistic Regression, GDA and Naïve Bayes have produced the lowest accuracies of 47%, 43% and 42%.

A new crack detection approach based on local binary patterns (LBP) with support vector machine (SVM) was proposed in this paper. The propsed algorithm can extract the LBP feature from each frame of the video taken from the road. Then, the dimension of the LBP feature spaces can be reduced by Principal Component Analysis(PCA). The simplified samples are trained to be decided the type of crack using Support Vector Machine(SVM). In order to reflect the directional imformation in detail, the LBP processed image is devided into nine sub-blocks. In this paper, driving tests were performed 10 times and 12,000 image data were applied to the proposed algorithm. The average accuracy of the proposed algorithm with sub-blocks is 91.91%, which is about 6.6% higher than the algorithm without sub-blocks. The LBP-PCA with SVM applying sub-blocks reflects the directional information of the crack so that it has high accuracy of 89.41% and 88.24%, especially in transverse and longitudinal cracks. In the performance analysis of different crack classifiers, the F-Measure, which considered balance between the precision and the recall, of alligator cracks classifier was the highest at 0.7601 and hence crack detection performance is higher than others.

Banana plantation is a commercial agricultural practice of huge significance especially in Asian and African countries. Banana production is affected by natural calamities and plant diseases. But plant diseases present a constant threat to the farmers affecting the quantity and quality of the banana cultivation. From the last decade, the image processing techniques and machine learning algorithms have been broadly used for identification and classification of infections in plants. In this work, texture pattern techniques for identification and classification of diseases in banana plants is introduced. The proposed methodology consists of two primary phases; (a) extraction of texture features from using local binary pattern (LBP); (b) classification of banana plant diseases and healthy banana plant. The texture features using LBP are extracted from an enhanced input image. The extracted features are fed to Support Vector Machine (SVM) and K-nearest neighbor (KNN) for final banana plant disease classification. The proposed technique is tested on the Plant Village dataset for the classification of two different experimental cases (i) Healthy-Black Sigatoka and (ii) Healthy-Cordana leaf spot. The proposed methodology attained an accuracy of 89.1 % and 90.9% for two experimental cases using SVM classifier.

In computer vision, Local Binary Pattern (LBP) and Scale Invariant Feature Transform (SIFT) are two widely used local descriptors. In this paper, we propose to combine them effectively for scene categorization. First, LBP and SIFT features are regularly extracted from training images for constructing a LBP feature codebook and a SIFT feature codebook. Then, a two-dimensional table is created by combining the obtained codebooks. For creating a representation for an image, LBP and SIFT features extracted from the same positions of the image are encoded together based on sparse coding by using the two-dimensional table. After processing all features in the input image, we adopt spatial max pooling to determine its representation. Obtained image representations are forwarded to a Support Vector Machine classifier for categorization. In addition, in order to improve the scene categorization performance further, we propose a method to select correlated visual words from large codebooks for constructing the two-dimensional table. Finally, for evaluating the proposed method, extensive experiments are implemented on datasets Scene Categories 8, Scene Categories 15 and MIT 67 Indoor Scene. It is demonstrated that the proposed method is effective for scene categorization.

Given the global spread of COVID-19, there is an urgent need for accurate point-of-care tests in settings where RT-PCR has a long turnaround time or is unavailable. Early screening with chest X-ray (CXR) imaging is attractive because it is widely available and low cost. Nevertheless, the manual interpretation of X-ray images is time consuming, subjective and necessitates expert radiological skills. We propose a lightweight, efficient automated COVID-19 detection model by combining handcrafted features with deep learning features to classify CXR images. Preprocessing normalizes image dimensions and enhances contrast. Local Binary Pattern (LBP) features are employed to enrapture local texture information of lung regions. For context-aware representation, we extract high-level features with a pretrained CNN (ResNet-50). The LBP and CNN features are fused into a single feature vector, which is fed into a support vector machine (SVM) classifier. We evaluated our proposed model on a public dataset available for COVID-19 radiography, containing normal, pneumonia, and other confirmed COVID-19 cases. The results show high classification performance with an accuracy of 97.91%, precision of 97.8%, recall of 97.83% and F1-score of 97.23%. The large burden of clinical screening demonstrates that the fusion of LBP and CNN features dramatically promotes the differentiability between infectious patients with COVID-19 and non-infected cases, which provides a valid approach to address scalable accuracy in clinical examination.

Local Binary Patterns (LBP) is an effective texture description operator and the histogram that it generates has been proved to be a very useful texture feature to adapt to rotation and illumination. Using the LBP features as feature vectors in adaBoost classifier for target identification has become a trend. But LBP is bound by the scale transformation, so it is not widely used in adaBoost face detector. This paper proposes a scale transform formula for Local Binary Patterns. Based on this formula, LBP features extracted from single fixed size templates can be trained to identify any size of faces. This paper also proposes a method to obtain particular detecting sub-areas called binary ring-shaped sub-windows, which can keep the LBP features rotation invariant. Experimental results show that the method we proposed here is feasible in face detecting.