Local Roughness Binary Pattern for Texture Classification

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.

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 Local Binary Pattern (LBP) and its variants are capable of extracting image texture and have been successfully applied to classification. However, LBP has not been used to extract and describe the texture of polarized images, and simple LBP cannot characterize the polarized texture information from different polarizations of angles. In order to solve these problems, we propose a new multi-angle orthogonal difference polarization image texture descriptor (MODP_ITD) by analyzing the relationship between the difference of orthogonal difference polarization images from different angles and the pixel intensity distribution in the local neighborhood of images from different angles. The MODP_ITD consists of three patterns: multi-angle polarization orthogonal difference local binary pattern (MODP_LBP), multi-angle polarization orthogonal difference local sampling point principal component sequence pattern (MODP_LPCSP) and multi-angle orthogonal difference polarization local difference binary pattern (MODP_LDBP). The MODP_LBP extracts local corresponding texture characteristics of polarized orthogonal difference images from multiple angles. The MODP_LPCSP sorts the principal component order of each angle orthogonal difference local sampling point. The MODP LDBP extracts the local difference characteristics between different angles by constructing a new polarized image. Then, the frequency histograms of MODP_LBP, MOD_LPCSP ,and MODP_LDBP are cascaded to generate MODP_ITD, so as to distinguish local neighborhoods. By using vertical and parallel polarization and unpolarized light active illumination, combined with the measurements at three different detection zenith angles, we constructed a polarization texture image database. A substantial number of experimental results on the self-built database show that our proposed MODP_ITD can represent the detailed information of polarization images texture. In addition, compared with the existing LBP methods, The MODP_ITD has a competitive advantage in classification accuracy.

The Local Binary Pattern (LBP) descriptor encodes the complementary information of the spatial patterns and intensity variations in a local image neighborhood. The richness of this multidimensional information offers many possible variations to the encoding process. Taking advantage of this, several variants of the LBP have been proposed. This work attempts to further optimize the discriminative power of the LBP specifically for the medical image classification task. It proposes an LBP variant that takes into account the salient edge features that are found particularly in chest X-ray images. In addition, it introduces a semi-global histogram to replace the commonly used global histogram which normally represents the spatial distribution of the generated codes in the LBP encoding process. The proposed LBP variant has been applied to the task of classifying X-rays of the ImageCLEFmed 2009 dataset into different chest categories. Experimental results show that while reducing the computational load the proposed LBP variant achieved an accuracy of 99.19% as compared to the best reported LBP based results of 98.73%, in classifying chest x-ray images of the ImageCLEFmed 2009 dataset.

Facial expression which carries rich information of body behavior is the leading carrier of human affective and the symbol of intelligence. The main purpose of this paper is to recognize 3D human facial expression. The research in this paper includes the expression feature extraction algorithm and fusion with different kinds of feature. To contain more local texture feature information, we proposed a new feature of 3D facial expression named Local Threshold Binary Pattern (LTBP) which based on Local Binary Pattern (LBP). We calculate the difference of gray value standard between neighboring pixels and the center pixel as a threshold to binary instead of the traditional LBP operation which only comparison of size between neighboring pixels and the center pixel. After we get the LTBP feature, we fuse the LTBP and HOG (Histogram of Oriented Gradient) features to get multi-feature fusion for 3D facial expression recognition. Our algorithm of 3D facial expression recognition comprises three steps: (1) extracting two sets of feature vectors and establishing the correlation criterion function between the two sets of feature vectors; (2) solving the two sets canonical projective vectors and extracting their canonical correlation features by the framework of canonical correlation analysis algorithm; (3) doing feature fusion for classification by using proposed strategy. We have performed comprehensive experiments on the BU-3DFE database which is presently the largest available 3D face database. We have achieved verification rates of more than 90% for the 3D facial expression recognition.

PLBP: An effective local binary patterns texture descriptor with pyramid representation

This paper introduces a new local feature description method to categorize scene images. We encode local image information by exploring the pseudo-Wigner distribution of images and the Local Binary Patterns (LBP) technique and make four major contributions. In particular, we first define a multi-neighborhood LBP for small image blocks. Second, we combine the multi-neighborhood LBP with the pseudo-Wigner distribution of images for feature extraction. Third, we derive the innovative WLBP feature vector by utilizing the frequency domain smoothing, the bag-of-words model and spatial pyramid representations of an image. Finally, we perform extensive experiments to evaluate the performance of the proposed WLBP descriptor. Specifically, we test our descriptor for classification performance using a Support Vector Machine (SVM) classifier on three fairly challenging publicly available image datasets, namely the UIUC Sports Event dataset, the Fifteen Scene Categories dataset and the MIT Scene dataset. Experimental results reveal that the proposed WLBP descriptor outperforms the traditional LBP technique and yields results better than some other popular image descriptors.

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.

In the digital age, some places have been installed with a surveillance camera, especially in a parking lot. In order to make use of that camera, a parking space detection is developed to help users find empty spaces. With so many paper that also studied this kind of system, we want to explore another feature extraction that could be used in this system. One particular feature extraction algorithm that we selected is quaternionic local ranking binary pattern (QLRBP). We also used local binary pattern (LBP) to compare it's performance. The result shows that QLRBP is able to achieve the accuracy score above 99%, but with expense of large feature vector size.

Biometrics is the study of methods of recognizing humans based on their behavioral and physical characteristics or traits. Face recognition is one of the biometric modalities that received a great amount of attention from many researchers during the past few decades because of its potential applications in a variety of security domains. Face recognition however is not only concerned with recognizing human faces, but also with recognizing faces of non-biological entities or avatars. Fortunately, the need for secure and affordable virtual worlds is attracting the attention of many researchers who seek to find fast, automatic and reliable ways to identify virtual worlds’ avatars. In this work, I propose new techniques for recognizing avatar faces, which also can be applied to recognize human faces. Proposed methods are based mainly on a well-known and efficient local texture descriptor, Local Binary Pattern (LBP). I am applying different versions of LBP such as: Hierarchical Multi-scale Local Binary Patterns and Adaptive Local Binary Pattern with Directional Statistical Features in the wavelet space and discuss the effect of this application on the performance of each LBP version. In addition, I use a new version of LBP called Local Difference Pattern (LDP) with other well-known descriptors and classifiers to differentiate between human and avatar face images. The original LBP achieves high recognition rate if the tested images are pure but its performance gets worse if these images are corrupted by noise. To deal with this problem I propose a new definition to the original LBP in which the LBP descriptor will not threshold all the neighborhood pixel based on the central pixel value. A weight for each pixel in the neighborhood will be computed, a new value for each pixel will be calculated and then using simple statistical operations will be used to compute the new threshold, which will change automatically, based on the pixel’s values. This threshold can be applied with the original LBP or any other version of LBP and can be extended to work with Local Ternary Pattern (LTP) or any version of LTP to produce different versions of LTP for recognizing noisy avatar and human faces images.

This paper proposes a novel approach in image compression based on Local Binary Pattern (LBP). LBP has already been used as a simple texture descriptor, labeling the image pixels by looking at the points surrounding a central point (usually on a 3×3 neighborhood) and examining whether these neighbors' color values are greater or less than the central point and accordingly assigning a binary value to the corresponding bit. The description of image's local pattern results in an eight-bit binary description, but in order to restore the image from such a LBP description, the value of each central pixel is also needed. These two pieces of information, i.e. the LBP description and the actual original value for each local neighborhood central pixel, are stored in a newly proposed Local Binary Compressed format, denoted .LBC, from which the image can be reconstructed by employing statistical methods, i.e. generating smaller or larger sets of random numbers to fill in the missing information within each local neighborhood, based on the LBP descriptor. Two statistical distributions were tested and, apart from the compression performance, a Structural Similarity Index Metric was used to evaluate the results.

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.

This paper presents an analysis of the statistical texture representation of the Local Binary Pattern (LBP) variants in the classification of wood defect images. The basic and variants of the LBP feature set that was constructed from a stage of feature extraction processes with the Basic LBP, Rotation Invariant LBP, Uniform LBP, and Rotation Invariant Uniform LBP. For significantly discriminating, the wood defect classes were further evaluated with the use of different classifiers. By comparing the results of the classification performances that had been conducted across the multiple wood species, the Uniform LBP was found to have demonstrated the highest accuracy level in the classification of the wood defects.

Breast cancer is one of the leading causes of death by cancer among women. The high mortality rates and the occurrence of this cancer worldwide show the importance of the investigation and development of means for the detection and early diagnosis of this disease. Computer-Aided Detection and Diagnosis systems have been developed to improve diagnostic accuracy by radiologists. This work proposes a method for discriminating patterns of malignancy and benignity of masses in digitized mammography images through the analysis of local features. The method comparatively applies the Scale-Invariant Feature Transform (SIFT), Speed Up Robust Feature (SURF), Oriented Fast and Rotated BRIEF (ORB) and Local Binary Pattern (LBP) descriptors for local feature extraction. These features are represented by a Bag of Features (BoF) model, applied to provide new representations of the data and to reduce its dimensionality. Finally, the features are used as input for the Support Vector Machine (SVM), Adaptive Boosting (Adaboost) and Random Forests (RF) classifiers to differentiate malignant and benign masses. The method obtained significant results, reaching 100% sensitivity, 99.65% accuracy and 99.24% specificity for benign and malignant mass classification.