Local Binary Pattern Variants Research Articles

Local binary pattern and its derivatives to handwriting-based gender classification

Several studies by psychologists and computer scientists have verified the link between handwriting and writer gender. The texture of the writing image is a major indicator of whether it is male or female writing. This paper conducts a comparison analysis to examine the effectiveness of various local binary patterns (LBPs) techniques in detecting gender from scanned images of handwriting. We study different LBP variants, including complete local binary pattern (CLBP), local ternary pattern (LTP), local configuration pattern (LCP), rotated local binary pattern (RLBP), local binary pattern variance (LBPV), and multi-scale local binary pattern (MLBP), as features for representing handwriting images. A support vector machine (SVM) is trained using features from male and female writing. The method achieves encouraging classification rates of 76.68 when tested on subsets of the Qatar University writer identification (QUWI) dataset containing English and Arabic writing samples when using the experimental protocols of the International Conference on Document Analysis and Recognition (ICDAR) 2013 gender classification competitions.

Radiomics for Parkinson's disease classification using advanced texture-based biomarkers

Parkinson's disease (PD) is one of the neurodegenerative diseases and its manual diagnosis leads to time-consuming process. MRI-based computer-aided diagnosis helps medical experts to diagnose PD more precisely and fast. Texture-based radiomic analysis is carried out on 3D MRI scans of T1 weighted and resting-state modalities. 43 subjects from Neurocon and 40 subjects from Tao-Wu dataset were examined, which consisted of 36 scans of healthy controls and 47 scans of Parkinson's patients. Total 360 2D MRI images are selected among around 17000 slices of T1-weighted and resting scans of selected 72 subjects. Local binary pattern (LBP) method was applied with custom variants to acquire advanced textural biomarkers from MRI images. LBP histogram helped to learn discriminative local patterns to detect and classify Parkinson's disease. Using recursive feature elimination, data dimensions of around 150-300 LBP histogram features were reduced to 13-21 most significant features based on score, and important features were analysed using SVM and random forest algorithms. Variant-I of LBP has performed well with highest test accuracy of 83.33%, precision of 84.62%, recall of 91.67%, and f1-score of 88%. Classification accuracies were obtained from 61.11% to 83.33% and AUC-ROC values range from 0.43 to 0.86 using four variants of LBP.•Parkinson's classification is carried out using an advanced biomedical texture feature. Texture extraction using four variants of uniform, rotation invariant LBP method is performed for radiomic analysis of Parkinson's disorder.•Proposed method with support vector machine classifier is experimented and an accuracy of 83.33% is achieved with 10-fold cross validation for detection of Parkinson's patients from MRI-based radiomic analysis.•The proposed predictive model has proved the potential of textures of extended version of LBP, which have demonstrated subtle variations in local appearance for Parkinson's detection.

An edge-located uniform pattern recovery mechanism using statistical feature-based optimal center pixel selection strategy for local binary pattern

The Local Binary Pattern (LBP) is a commonly used method for texture classification that performs well in terms of feature discrimination. However, (1) LBP can misclassify some important edge-located textures as non-uniform patterns with only one bin in the feature histogram, thus losing their discrimination capability. (2) When center pixel is contaminated by noise, a uniform pattern may be transformed into a non-uniform pattern, which can seriously affect the obtained LBP, thus degrading the classification results. To overcome these drawbacks, an edge-located uniform pattern recovery mechanism using statistical feature-based optimal center pixel selection strategy (SFB-OCPS) is proposed in this paper.To extract the correct edge pixels, we divide the whole texture image into 16 = 4 × 4 sub-images and propose an edge pixel selection strategy (EPSS) based on adaptive quantization with local threshold on each sub-image. Then 3 candidate center pixels constructed by statistical features of the local sampling neighborhood are generated for each edge-located center pixel. After the steps above, the SFB-OCPS strategy is introduced into the LBP-based algorithms. It is possible to recover some important edge-located non-uniform patterns to uniform patterns with an optimal center pixel selection, thus improving feature discrimination capability of the LBP-based algorithms.It should be emphasized that any LBP variants can introduce the proposed SFB-OCPS strategy to achieve the recovery of the edge-located uniform patterns. To validate the effectiveness of the proposed SFB-OCPS strategy, we introduce the SFB-OCPS strategy into the original LBP and 5 representative LBP-based algorithms. Experiments are conducted on 6 representative texture databases. Classification comparison reveals that the introduction of SFB-OCPS strategy can significantly improve the texture classification performance of LBP-based algorithms. Additionally, the noise-robustness of the proposed SFB-OCPS strategy is also verified through a series of experiments.

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.

A Novel Efficient Video Smoke Detection Algorithm Using Co-occurrence of Local Binary Pattern Variants

A Novel Efficient Video Smoke Detection Algorithm Using Co-occurrence of Local Binary Pattern Variants

A New LBP Variant: Corner Rhombus Shape LBP (CRSLBP).

The local binary model is a straightforward, dependable, and effective method for extracting relevant local information from images. However, because it only uses sign information in the local region, the local binary pattern (LBP) is ineffective at capturing discriminating characteristics. Furthermore, most LBP variants select a region with one specific center pixel to fill all neighborhoods. In this paper, a new variant of a LBP is proposed for texture classification, known as corner rhombus-shape LBP (CRSLBP). In the CRSLBP approach, we first use three methods to threshold the pixel’s neighbors and center to obtain four center pixels by using sign and magnitude information with respect to a chosen region of an even block. This helps determine not just the relationship between neighbors and the pixel center but also between the center and the neighbor pixels of neighborhood center pixels. We evaluated the performance of our descriptors using four challenging texture databases: Outex (TC10,TC12), Brodatz, KTH-TIPSb2, and UMD. Various extensive experiments were performed that demonstrated the effectiveness and robustness of our descriptor in comparison with the available state of the art (SOTA).

Using global information to refine local patterns for texture representation and classification

Local binary pattern (LBP) and its variants have been successfully applied in texture feature extraction. However, it is hard for most LBP-based methods to effectively describe and distinguish the local neighborhoods with similar structures (that is, the calculated feature patterns are identical) but different contrasts or grayscales. To alleviate such problems, we propose a novel global refined local binary pattern (GRLBP) by analyzing the nature of pixel intensity distribution in local neighborhoods. GRLBP consists of two descriptors called magnitude refined local sign binary pattern (MRLBP_S) and center refined local magnitude binary pattern (CRLBP_M). MRLBP_S distinguishes local neighborhoods with contrast differences by using global magnitude anchors to refine local sign patterns. And CRLBP_M identifies local neighborhoods with grayscale differences by employing global central grayscale anchors to refine local magnitude patterns. Finally, frequency histograms of MRLBP_S and CRLBP_M from each image are cascaded to generate the GRLBP. Extensive experimental results on seven benchmark texture databases: Outex, CUReT, KTH-TIPS, UMD, UIUC, KTH-T2b, and DTD demonstrate that the proposed GRLBP can represent the detailed information of texture images. Furthermore, compared with state-of-the-art LBP variants, GRLBP has competitive advantages in classification accuracy, feature dimension, and computational complexity, respectively.

Applicability of deep neural networks and local binary pattern for the classification of MCI‐C and MCI‐NC

AbstractBackgroundThe classification of Mild Cognitive Impairment (MCI) patients, who convert to Alzheimer’s (MCI‐C) and do not convert to Alzheimer’s (MCI‐NC) is important to understand the progression of the disease. The imaging data, particularly s‐MRI helps to understand the disease by capturing the variation in the gray matter.MethodWe developed a Deep Learning based method to classify MCI‐C and MCI‐NC. For each MRI volume Local Binary Pattern (LBP) was applied on each slice and the features so obtained were concatenated to get the feature vector of the volume. The features were obtained using three methods: creating a histogram having 256 bins, by using the Uniform LBP, and using the Uniform‐Rotation Invariant LBP. This feature set was fed to a Deep Neural Network having two hidden layers and a Softmax layer. Experiments were conducted to find the optimal architecture of the DNN, which gives the best accuracy. Also, a dropout layer was added to prevent overfitting. The results were also compared with the Support Vector Machine. The data used in the experiment was obtained from Alzheimer’s Disease Neuroimaging Initiative (ADNI).ResultWe evaluated the model performance using accuracy. It was found that amongst the three variants of LBP, that having 256 bins produced the best results. Also, DNN performed far better as compared to SVM. Amongst the various DNN’s the one having 100 units in the first hidden layer, 50 in the next, and having a dropout of 0.1, gave the best results. The model used Adam optimizer and categorical cross‐entropy. It was also noted that the results obtained using raw volumes were inferior as compared to that obtained using LBP. Table 1 shows the results.ConclusionWe propose a Deep Learning model for classifying MCI‐C and MCI‐NC. The proposed method is capable of finding discriminating features to classify the two sets. It may be the case that LBP is able to infer the difference in the distribution of microstructures in the two classes which helped the DNN to obtain better accuracy as compared to the state of the art.

A local binary patterns/variance operator based on guided filtering for seismic fault detection

Aiming at suppressing noise interference, improving the fault detection ability of seismic data, fully excavating the effective information in seismic data, and further improving the accuracy of fault detection, this study proposes a seismic fault detection method that combines the local binary pattern/variance (LBP/VAR) operator with guided filtering. The proposed method combines the advantages of LBP/VAR and guided filtering to remove noise from seismic data, and can simultaneously smooth the data and preserve linear features. When compared with several existing methods (coherent operator, LBP/VAR operator, LBP/VAR operator based on median filtering, and Canny operator based on guided filtering), the proposed method exhibits a better SNR, a better ability to identify small faults, and robustness to noise. This novel algorithm can control the balance between noise attenuation and effective signal preservation as well as effectively detect faults in seismic data. Therefore, the proposed method effectively improves the fault identification accuracy, facilitates the gas-bearing analysis of the structure, provides guidance for the actual well location deployment of the project, and has important practical significance for oil and gas exploration and development.

A novel filtering kernel based on difference of derivative Gaussians with applications to dynamic texture representation

Efficiently representing spatio-temporal features of dynamic textures (DTs) in videos has been restricted due to negative impacts of the well-known issues of environmental changes, illumination, and noise. In order to mitigate those, this paper proposes a new approach for an efficient DT representation by addressing the following novel concepts. Firstly, a novel filtering kernel, called Difference of Derivative Gaussians (DoDG), is introduced for the first time based on high-order derivative of a Gaussian kernel. It allows to point out DoDG-based filtered outcomes which are prominently resistant to noise for DT representation compared to exploiting the conventional Difference of Gaussians (DoG). A new framework in low computational complexity is then presented to take DoDG into account video denoising as an effective preprocessing of DT encoding. Finally, a simple variant of Local Binary Patterns (LBPs) is addressed to extract local features from these DoDG-filtered outcomes for constructing discriminative DoDG-based descriptors in small dimension, expected as one of appreciated solutions for mobile applications. Experimental results for DT recognition have verified that our proposal significantly performs well compared to all non-deep-learning methods, while being very close to deep-learning approaches. Also, ours are eminently better than those based on the traditional DoG.

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.

Adaptive center pixel selection strategy in Local Binary Pattern for texture classification

Local Binary Pattern (LBP) is widely used in texture classification because of its powerful capability to extract texture features of a center pixel. However, LBP has three main drawbacks: (1) limited by the low resolution of imaging device, the quality of texture image is degraded, and some real existing pixels with more texture information are unavoidably lost. (2) Center pixel gc is the most important factor to extract correct LBP pattern. However, by far LBP and its variants do not show any solutions to enhance the robustness of center pixel gc. (3) Some LBP patterns include important texture microstructures, but they are ignored by uniform patterns. At the same time, some uniform patterns can be corrupted by noise and misclassified into non-uniform patterns. These LBP patterns therefore all lost their discrimination capability. In order to overcome these three disadvantages, in this paper, we propose a novel adaptive center pixel selection (ACPS) strategy.Inspired by image super-resolution techniques, ACPS firstly applies the interpolation method to recover the lost real existing pixels and generate the center pixel candidates with more texture information. Then, the gradient information is used to obtain the edge image aiming to find the non-uniform patterns at edge points which may contain complicated texture microstructures. After generating the center pixel candidates and edge image, we introduce ACPS strategy into the LBP framework. By adaptively selecting the optimal center pixel from all center pixel candidates, one non-uniform pattern at the edge point can be possibly recovered to the uniform pattern, and regain its discrimination power. It is worth noting that any other LBP variants can also employ the ACPS strategy to more effectively extract its texture features. By observing the experimental results on representative texture databases of Outex, UIUC, CUReT, XU_HR, ALOT and KTHTIPS2b after introducing the ACPS strategy into LBP and its variants of LTP, CLBP, BRINT, CRDP, FbLBP, and CJLBP, the texture classification performances can be significantly improved.

Neighborhood and center difference-based-LBP for face recognition

This research paper introduces the novel local binary pattern (LBP) variant for face recognition (FR) called as neighborhood and center difference-based-LBP (NCDB-LBP). In NCDB-LBP, the 4 labeled function is proposed to capture the robust features from 3 × 3 pixel window. For each neighborhood position , 2 first-order derivatives are computed, first computed between the adjacent neighborhood and the current neighborhood and the second computed between the center pixel and the current neighborhood. Employing the proposed function between the 2 first-order derivatives (produced from each neighborhood position) eventually results in 4 labeled window. All 8 neighborhoods are then placed in the 1 × 8 pixel window from which the 4 different binary patterns are produced. This concept is performed in both anticlockwise (ac) and clockwise (c) directions, termed as NCDB-LBPac and NCDB-LBPc descriptors. After binary patterns are encoded for each pixel position, the 4 transformed images are produced from ac direction and 4 from the c direction. All the respective directional transformed images are then divided into 3 × 3 subregions for histogram extraction. The combined histograms from all the respective subregions are the entire feature size of the NCDB-LBPac and NCDB-LBPc descriptors. To reduce the feature size, PCA and FLDA are utilized. Finally, classification is performed by SVMs and NN. The proposed FR approach is tested on ORL, GT, JAFFE, Yale, YB and EYB databases. The proposed FR approach achieves encouraging results.

Palmprint recognition using state‐of‐the‐art local texture descriptors: a comparative study

Several human being traits can be used as a robust and distinctive identifier for a given person. The palm region of the hand is one of these features that researchers in biometric fields have given a huge consideration in recent years. Many works have been proposed in the literature to design palmprint (an image acquired of the palm region) recognition framework. Extraction of prominent image local features is a critical module in most of these approaches. Local Binary Patterns (LBP) like methods, have emerged as one of the most effective feature extraction techniques. Despite a period of remarkable evolution, neither extensive and comprehensive evaluation nor comparison has been performed to date on a large number of LBP variants and non-LBP texture methods in palmprint recognition problem. Motivated by this, this paper aims to fill that gap and provide a comprehensive comparative study of the performance of a large number of recent texture descriptors in palmprint recognition. Extensive experimental results on the well-known constrained and unconstrained challenging palmprint databases, indicate that a number of tested local texture descriptors, which are evaluated for the first time on palmprint recognition, achieve promising results. Classification results are statistically compared through Wilcoxon signed rank test.

Classification of wood defect images using local binary pattern variants

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.

A combination of 3-D discrete wavelet transform and 3-D local binary pattern for classification of mild cognitive impairment

BackgroundThe detection of Alzheimer’s Disease (AD) in its formative stages, especially in Mild Cognitive Impairments (MCI), has the potential of helping the clinicians in understanding the condition. The literature review shows that the classification of MCI-converts and MCI-non-converts has not been explored profusely and the maximum classification accuracy reported is rather low. Thus, this paper proposes a Machine Learning approach for classifying patients of MCI into two groups one who converted to AD and the others who are not diagnosed with any signs of AD. The proposed algorithm is also used to distinguish MCI patients from controls (CN). This work uses the Structural Magnetic Resonance Imaging data.MethodsThis work proposes a 3-D variant of Local Binary Pattern (LBP), called LBP-20 for extracting features. The method has been compared with 3D-Discrete Wavelet Transform (3D-DWT). Subsequently, a combination of 3D-DWT and LBP-20 has been used for extracting features. The relevant features are selected using the Fisher Discriminant Ratio (FDR) and finally the classification has been carried out using the Support Vector Machine.ResultsThe combination of 3D-DWT with LBP-20 results in a maximum accuracy of 88.77. Similarly, the proposed combination of methods is also applied to distinguish MCI from CN. The proposed method results in the classification accuracy of 90.31 in this data.ConclusionThe proposed combination is able to extract relevant distribution of microstructures from each component, obtained with the use of DWT and thereby improving the classification accuracy. Moreover, the number of features used for classification is significantly less as compared to those obtained by 3D-DWT. The performance of the proposed method is measured in terms of accuracy, specificity and sensitivity and is found superior in comparison to the existing methods. Thus, the proposed method may contribute to effective diagnosis of MCI and may prove advantageous in clinical settings.

Robust Texture Description Using Local Grouped Order Pattern and Non-Local Binary Pattern

Local binary pattern (LBP) and its many variants have shown effectiveness for texture classification. However, most of these LBP methods focus on encoding local intensity differences between a central pixel and its neighboring sampling points and consequently have two major problems: 1) they are unable to describe the intensity order relationships among neighboring sampling points, and 2) they fail to capture long-range pixel interactions that take place outside a compact neighborhood. In view of these problems, in this paper we propose two novel operators, called local grouped order pattern (LGOP) and non-local binary pattern (NLBP), for texture description. For the first problem, LGOP groups the neighboring sampling points by referring to a dominant direction and encodes the groupwise intensity order relationships. For the second problem, NLBP computes several anchors based on global image statistics and progressively encodes non-local intensity differences between the neighboring sampling points and anchors. Finally, we combine LGOP and NLBP via central pixel encoding to construct discriminative histogram features as texture descriptor LGONBP. Experiments on four texture benchmark databases (i.e., Outex, CUReT, UMD and KTH-TIPS) demonstrate the superiority of LGONBP over state-of-the-art LBP variants for texture classification under both noise-free and noisy conditions. The code is available at <uri>https://github.com/stc-cqupt/LGONBP</uri>.

Local Binary Patterns and Its Extended Variants

ABSTRACT This paper focuses on the Local Binary Patterns and its various important variants. LBP is a non-parametric descriptor and used to extract, analyze, recognize and classify the different modality images. It summarizes the local patterns of image characteristics efficiently. LBP and its many extended versions have been extensively used in numerous applications of computer vision, image processing, pattern recognition and biomedical field in recent years. Very discriminative and computationally efficient local texture descriptors based on local binary patterns (LBPs) is studied, which led to significant progress in applying texture methods to different problems and applications. The efficiency and usability of the LBP operator and its success in various real world applications has inspired the development of much new powerful LBP variants. In this paper, the important extensions of LBP using local structure of the image are extensively reviewed. Keywords: Local binary pattern, texture, LBP, LTP

Scale-adaptive local binary pattern for texture classification

Local binary pattern (LBP) has already been proved to be a powerful measure of image texture with fixed sampling scheme: all P neighbor pixels in a single-scale are usually sampled by using a fixed radius R. It can effectively address grayscale and rotation variations. However, the LBP method is sensitive to image noise and fails to achieve desirable performance for texture classification with significant scale changes. With the aim to deal with these disadvantages of LBP, a new method named scale-adaptive local binary pattern (SALBP) is proposed in this paper. The essence of our proposed SALBP method is to adaptively find a single but optimal scale from multiple scales for each radial direction in accordance with the characteristics of a local area. First, we select candidates of neighbors by the majority vote strategy for signs. Second, we determine final neighbor pixels by the maximal difference magnitude selection strategy for magnitudes of candidates. This procedure lets each neighbor pixel to adaptively obtain its own optimal scale from multiple scales, which refers to the adaptive optimal sampling radius. Therefore, scale invariance can be significantly improved. Extensive experiments on four public texture databases (i.e., Outex, CUReT, UIUC and Brodatz) demonstrate that the proposed SALBP method achieves significantly better results than representative LBP variants for texture classification tasks with a smaller feature dimension.

Tampering detection using hybrid local and global features in wavelet-transformed space with digital images

The widespread availability of advanced digital image technology and powerful image editing tools has made it extremely easy to manipulate image content. One popular technique that is challenging for tampering detection methods is the copy–move forgery. Here, one part of the image is copied and is pasted into another part of the same image. Image tampering is very difficult to detect with the naked eye. The authenticity of digital images is critical when they are used as evidence in court, for news reports or insurance claims, as they have the power to influence decisions and outcomes. Hence, this paper presents an efficient method for copy–move forgery detection by means of a HOG descriptor and local binary pattern variance algorithms. The copy–move forgery detection (CMFD) approach is introduced and is applied to the forged region by determining suitable post-processing techniques. The proposed technique is evaluated using MICC-F220, MICC-F2000, UCID, CoMoFoD and CASIA TIDE data sets in which translation, flipping, rotation, scaling, color reduction, brightness change and JPEG compression are applied to an image. The experimental performance of the proposed technique is assessed in terms of the true- and false-detection rates. Ultimately, our proposed method is highly suitable for detecting the altered region, and accurate CMFD results are obtained in forensic image applications.