Local Binary Pattern Variants Research Articles

2D-LBP: An Enhanced Local Binary Feature for Texture Image Classification

The local binary pattern (LBP) and its variants have shown the effectiveness in texture images classification, face recognition, and other applications. However, most of these LBP methods only focus on the histogram of LBP patterns and ignore the spatial contextual information between LBP patterns. In this paper, we propose a 2D-LBP method which uses a sliding window to count the weighted occurrence number of the rotation invariant uniform LBP pattern pairs to obtain the spatial contextual information. The multi-resolution 2D-LBP features can also be obtained when the radius of 2D-LBP is changed. At last, a two-stage classifier which acts as an ensemble learning step is followed to achieve an accurate classification by combining the predictions on each 2D-LBP with single resolution. Theoretical proof shows that the proposed 2D-LBP is a general framework and can be integrated on other LBP variants to derive new feature extraction methods. Experimental results show that, the proposed method achieves 99.71%, 97.09%, 98.48%, and 49.00% classification accuracy on the public “Brodatz,” “CUReT,” “UIUC,” and “FMD” texture image databases, respectively. Compared with the original LBP and its variants, the proposed method obtains higher classification accuracy under different cases, and simultaneously owns shorter time complexity.

A Multiscale Hierarchical Threshold-Based Completed Local Entropy Binary Pattern for Texture Classification

Over the year, visual texture analysis has come to be recognized as one of the most important methods in the area of medical image analysis and understanding, face description and detection, and so on. The goal of texture descriptors is to capture the general characteristic of textures such as dependency as well as invariance properties. Among all the texture descriptors, the binary pattern family of algorithms achieves a great trade of representation efficiency and complexity. This work introduces an efficient discriminative texture descriptor for visual texture classification. Its main contribution is twofold: a multiscale thresholding framework based on hierarchical adaptive local partition to binary encoding and an efficient completed local entropy binary pattern (CLEBP) descriptor. The basic completed local entropy binary pattern is extended by multiscale thresholding framework with hierarchical thresholding to capture not only microstructure local patterns but also macrostructure texture information. Such extension improves the quality and discriminative factor of texture classification. Extensive experiments on three widely used benchmark texture databases (Outex, UIUC, and KTH-TIPS) proof the efficiency of the proposed visual texture descriptor and hierarchical thresholding strategy. Compared with some classical local binary pattern variants and many state-of-the-art methods, the proposed descriptor achieves competitive and superior texture classification performance. The results prove that the proposed method is a powerful and effective texture descriptor for visual texture classification.

Animal Sound Recognition Based on Double Feature of Spectrogram

Due to existence of different environments and noises, the existing method is difficult to ensure the recognition accuracy of animal sound in low Signal-to-noise (SNR) conditions. To address these problems, we propose a double feature, which consists of projection feature and Local binary pattern variance (LBPV) feature, combined with Random forest (RF) for animal sound recognition. In feature extraction, an operation of projecting is made on spectrogram to generate the projection feature. Meanwhile, LBPV feature is generated by means of accumulating the corresponding variances of all pixels for every Uniform local binary pattern (ULBP) in the spectrogram. Short-time spectral estimation algorithm is used to enhance sound signals in severe mismatched noise conditions. In the experiments, we classify 40 kinds of common animal sounds under different SNRs with rain noise, traffic noise, and wind noise. As the experimental results show, the proposed framework consisting of shorttime spectrum estimation, double feature, and RF, can recognize a wide range of animal sounds and still remains a recognition rate over 80% even under 0dB SNR.

Face Anti-Spoofing Using Texture-Based Techniques and Filtering Methods

User authentication for an accurate biometric system is the demand of the hour in today’s world. When somebody attempts to take on the appearance of another person by introducing a phony face or video before the face detection camera and gets illegitimate access, a face presentation attack usually happens. To effectively protect the privacy of a person, it is very critical to build a face authentication and anti-spoofing system. This paper introduces a novel and appealing face spoof detection technique, which is primarily based on the study of contrast and dynamic texture features of both seized and spoofed photos. Valid identification of photo spoofing is anticipated here. A modified version of the DoG filtering method, and local binary pattern variance (LBPV) based technique, which is invariant to rotation, are designated to be used in this paper. Support vector machine (SVM) is used when feature vectors are extracted for further analysis. The publicly available NUAA photo-imposter database is adapted to test the system, which includes facial images with different illumination and area. The accuracy of the method can be assessed using the false acceptance rate (FAR) and false rejection rate (FRR). The results express that our method performs better on key indices compared to other state-of-the-art techniques following the provided evaluation protocols tested on a similar dataset.

Image Spoofing Detection Using Local Binary Pattern and Local Binary Pattern Variance

Particularly in the field of biometric security using human face has been widely implemented in the real world. Currently the human face is one of the guidelines in the security system. Nowadays the challenge is how to detect data falsification; such an attack is called spoofing. Spoofing occurs when someone is trying to pretend to be someone else by falsifying the original data and then that person may gain illegal access and benefit him. For example one can falsify the face recognition system using photographs, video, masks or 3D models. In this paper image spoofing human face detection using texture analysis on input image is proposed. Texture analysis used in this paper is the Local Binary Pattern (LBP) and Local Binary Pattern Variance (LBPV). To classified input as original or spoof K-Nearest Neighbor (KNN) used. Experiment used 5761 spoofs and 3362 original from NUAA Imposter dataset. The experimental result yielded a best success rate of 87.22% in term of accuracy with configuration of the system using LBPV and histogram equalization with ratio 𝑅 = 7 and 𝑃 = 8.

Generalized Completed Local Binary Patterns for Time-Efficient Steel Surface Defect Classification

Efficient defect classification is one of the most important preconditions to achieve online quality inspection for hot-rolled strip steels. It is extremely challenging owing to various defect appearances, large intraclass variation, ambiguous interclass distance, and unstable gray values. In this paper, a generalized completed local binary patterns (GCLBP) framework is proposed. Two variants of improved completed local binary patterns (ICLBP) and improved completed noise-invariant local-structure patterns (ICNLP) under the GCLBP framework are developed for steel surface defect classification. Different from conventional local binary patterns variants, descriptive information hidden in nonuniform patterns is innovatively excavated for the better defect representation. This paper focuses on the following aspects. First, a lightweight searching algorithm is established for exploiting the dominant nonuniform patterns (DNUPs). Second, a hybrid pattern code mapping mechanism is proposed to encode all the uniform patterns and DNUPs. Third, feature extraction is carried out under the GCLBP framework. Finally, histogram matching is efficiently accomplished by simple nearest-neighbor classifier. The classification accuracy and time efficiency are verified on a widely recognized texture database (Outex) and a real-world steel surface defect database [Northeastern University (NEU)]. The experimental results promise that the proposed method can be widely applied in online automatic optical inspection instruments for hot-rolled strip steel.

Comparative Study on Local Binary Patterns for Mammographic Density and Risk Scoring.

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.

Texture classification using optimized local ternary patterns with nonlinear diffusion as pre-processing

The main focus of this paper is to improve the performance of the texture model Optimized Local Ternary Patterns (OLTP), which is known as one of the successful variants of the texture model Local Binary Patterns (LBP), a well known method for texture analysis and its applications. Generally preprocessing is used in digital image processing for reducing the unwanted noise and disturbances in such a way that it improves the quality of the image. Preprocessing not only removes the distortions but also enhances the features of the image for further processing. To achieve better recognition accuracy, the texture model OLTP was combined with a preprocessing method that uses nonlinear diffusion method as a preprocessing tool in this paper, with the hope that this idea will surely improve the local feature description and texture classification process. This nonlinear diffusion method uses two newly developed edge stopping functions for preprocessing. This proposed method is tested with two standard texture datasets namely Brodatz Dataset and Usptex dataset. The results show that the use of the preprocessing step really improved the texture classification accuracy.

基于局部二进制模式方差的分数阶微分医学图像增强算法

基于局部二进制模式方差的分数阶微分医学图像增强算法

Computer-aided classification of the mitral regurgitation using multiresolution local binary pattern

This paper introduces a computer-aided classification (CAC) system for the severity analysis of mitral regurgitation (MR) utilizing multiresolution local binary pattern variants texture features. Initially, the Gaussian pyramid has been used as a multiresolution technique. Subsequently, seven variants of the local binary pattern (LBP) have been employed to extract the features. At last, support vector machine and random forest classifiers are used for classification. The performances of conventional LBP variants and proposed features have been evaluated on MR image database in three classes, i.e., mild, moderate, and severe, in three different views. The Gaussian pyramid-based center-symmetric local binary pattern performed well in all three views. The achieved classification accuracies are 95.66 ± 0.98% in the apical 2 chamber, 94.47 ± 1.91% in the apical 4 chamber and 94.21 ± 1.31% in parasternal long axis views using SVM classifier with the tenfold cross-validation. The outcomes of paper confirm that the performance of the conventional LBP features is enhanced significantly and the proposed CAC system is useful in assisting cardiologists in the severity analysis of MR.

Central pixel selection strategy based on local gray-value distribution by using gradient information to enhance LBP for texture classification

Local binary pattern (LBP) has been successfully used in computer vision and pattern recognition applications, such as biomedical image analysis, remote sensing and image retrieval. However, the current LBP-based features, which assign a fixed sampling radius for all pixels in a single scale, completely ignore the fact that different central pixels actually have different local gray-value distributions and the proper sampling radius should be different for pixels. In this paper, we propose a novel and effective central pixel selection (CPS) strategy by using gradient information to classify central pixels of a texture image into different classes based on their local gray-value distributions. Then, we introduce this CPS strategy into the LBP framework and assign an adaptive sampling radius for each central pixel according to the class it belongs to. As a preprocessing step of LBP framework, this CPS strategy can also be integrated into any other LBP variants so as to extract more effective local texture features. Extensive experiments on five representative texture databases of Outex, UIUC, CUReT, UMD and ALOT validate the efficiency of the proposed central pixel selection (CPS) strategy, which can achieve almost 16% improvement over the original LBP and 1%–10% improvement compared with the best classification accuracy among other benchmarked state-of-the-art LBP variants.

Encoding pairwise Hamming distances of Local Binary Patterns for visual smoke recognition

To achieve scale invariance, existing methods based on multi-scale local binary patterns (LBP) usually concatenate histograms of LBP codes from different scales. Direct concatenation of histograms is very simple and computationally efficient, but it cannot well model the spatial relationship of LBP codes across scales. Aiming at modeling scale-level variations of LBP codes, we measure and encode the relationship between a pair of LBP codes at the same position from two scales. Gaussian filters are applied to generate the scale space of an image, and original LBP codes are extracted on each scale. The Hamming distance between a pair of LBP codes is used to measure variations of LBP codes across scales. To incorporate the scale-level variations of LBPs into codes, we encode the Hamming distance measures in the same way as LBP to generate a novel code, called Pairwise Comparing Local Binary Patterns (PCLBP). To achieve rotation invariance, LBP and PCLBP codes are aligned to the direction with the maximum magnitude of local differences between a center pixel and its neighbors. To improve reliability of the alignments, we also propose a circular Gaussian smoothing method to remove noise in local differences. Finally, we concatenate the histograms of aligned PCLBP and LBP codes to obtain rotation and scale invariant descriptors for smoke classification. Extensive experiments show that our method obviously outperforms existing LBP variants on smoke datasets, and also achieves outstanding performance on other datasets.

Local mean differential excitation pattern for content based image retrieval

Effective and facile local texture feature descriptors assume a significant role in many image classification and retrieval tasks. However, conventional feature descriptors are impaired to capture salient image features like local inherent structure, orientation and edge information of the image. A typical local binary pattern (LBP)-like feature descriptors elicits information based on the gray level difference from each locality and consequently its value immensely susceptible to noise. To overcome a few deficiencies of traditional methods, the proposed research work acquaint with a lucid, novel, yet robust texture feature descriptor called local mean differential excitation pattern (LMDeP) for efficient content based image retrieval. The main strategy of LMDeP is to elucidate differential excitation using the mean of points over each angular and radial neighbor points. This enables the LMDeP to fetch robust features by skimming the noise effect of enticing neighbors over each local patch. LMDeP performance compared to the LBP variants on Corel-1 K and Corel-5 K datasets establish the superiority of proposed method to its counterparts.

Ear recognition using local binary patterns: A comparative experimental study

Identity recognition using local features extracted from ear images has recently attracted a great deal of attention in the intelligent biometric systems community. The rich and reliable information of the human ear and its stable structure over a long period of time present ear recognition technology as an appealing choice for identifying individuals and verifying their identities. This paper considers the ear recognition problem using local binary patterns (LBP) features. Where, the LBP-like features characterize the spatial structure of the image texture based on the assumption that this texture has a pattern and its strength (amplitude)-two locally complementary aspects. Their high discriminative power, invariance to monotonic gray-scale changes and computational efficiency properties make the LBP-like features suitable for the ear recognition problem. Thus, the performance of several recent LBP variants introduced in the literature as feature extraction techniques is investigated to determine how can they be best utilized for ear recognition. To this end, we carry out a comprehensive comparative study on the identification and verification scenarios separately. Besides, a new variant of the traditional LBP operator named averaged local binary patterns (ALBP) is proposed and its ability in representing texture of ear images is compared with the other LBP variants. The ear identification and verification experiments are extensively conducted on five publicly available constrained and unconstrained benchmark ear datasets stressing various imaging conditions; namely IIT Delhi (I), IIT Delhi (II), AMI, WPUT and AWE. The obtained results for both identification and verification indicate that the current LBP texture descriptors are successful feature extraction candidates for ear recognition systems in the case of constrained imaging conditions and can achieve recognition rates reaching up to 99%; while, their performance faces difficulties when the level of distortions increases. Moreover, it is noted that the tested LBP variants achieve almost close performance on ear recognition. Thus, further studies on other applications are needed to verify this close performance. We believe that the presented study has significant insights and can benefit researchers in choosing between LBP variants as well as acting as a connection between previous studies and future work in utilizing LBP-like features in ear recognition systems.

Some variants of spiral LBP in texture recognition

Texture classification is one of the recently popular study topics in pattern recognition. Local binary pattern (LBP) is a very efficient local texture descriptor and is used for feature extraction in texture recognition. There are five main steps in representation of texture images: neighbourhood topology and sampling, thresholding and quantisation, encoding and regrouping, combining complementary features. In this study, the authors used symmetric two spirals LBP to measure the grey-scale difference between the centre pixel and its neighbours. They also extended the proposed method by using four spirals LBP to generate the LBP code. For classification, linear regression classification method, which is generally used to solve the face recognition problems, is used. The authors tested the performance of their method on UIUC and CUReT texture image databases. It is experimentally demonstrated that the proposed method achieves the highest classification accuracy among the comparative methods on texture databases.

A novel method for content-based image retrieval to improve the effectiveness of the bag-of-words model using a support vector machine

The advancements in the multimedia technologies result in the growth of the image databases. To retrieve images from such image databases using visual attributes of the images is a challenging task due to the close visual appearance among the visual attributes of these images, which also introduces the issue of the semantic gap. In this article, we recommend a novel method established on the bag-of-words (BoW) model, which perform visual words integration of the local intensity order pattern (LIOP) feature and local binary pattern variance (LBPV) feature to reduce the issue of the semantic gap and enhance the performance of the content-based image retrieval (CBIR). The recommended method uses LIOP and LBPV features to build two smaller size visual vocabularies (one from each feature), which are integrated together to build a larger size of the visual vocabulary, which also contains complementary features of both descriptors. Because for efficient CBIR, the smaller size of the visual vocabulary improves the recall, while the bigger size of the visual vocabulary improves the precision or accuracy of the CBIR. The comparative analysis of the recommended method is performed on three image databases, namely, WANG-1K, WANG-1.5K and Holidays. The experimental analysis of the recommended method on these image databases proves its robust performance as compared with the recent CBIR methods.

Tool wear monitoring using an online, automatic and low cost system based on local texture

Abstract In this work we propose a new online, low cost and fast approach based on computer vision and machine learning to determine whether cutting tools used in edge profile milling processes are serviceable or disposable based on their wear level. We created a new dataset of 254 images of edge profile cutting heads which is, to the best of our knowledge, the first publicly available dataset with enough quality for this purpose. All the inserts were segmented and their cutting edges were cropped, obtaining 577 images of cutting edges: 301 functional and 276 disposable. The proposed method is based on (1) dividing the cutting edge image in different regions, called Wear Patches (WP), (2) characterising each one as worn or serviceable using texture descriptors based on different variants of Local Binary Patterns (LBP) and (3) determine, based on the state of these WP, if the cutting edge (and, therefore, the tool) is serviceable or disposable. We proposed and assessed five different patch division configurations. The individual WP were classified by a Support Vector Machine (SVM) with an intersection kernel. The best patch division configuration and texture descriptor for the WP achieves an accuracy of 90.26% in the detection of the disposable cutting edges. These results show a very promising opportunity for automatic wear monitoring in edge profile milling processes.

Computer-aided diagnosis of lung nodule using gradient tree boosting and Bayesian optimization.

We aimed to evaluate a computer-aided diagnosis (CADx) system for lung nodule classification focussing on (i) usefulness of the conventional CADx system (hand-crafted imaging feature + machine learning algorithm), (ii) comparison between support vector machine (SVM) and gradient tree boosting (XGBoost) as machine learning algorithms, and (iii) effectiveness of parameter optimization using Bayesian optimization and random search. Data on 99 lung nodules (62 lung cancers and 37 benign lung nodules) were included from public databases of CT images. A variant of the local binary pattern was used for calculating a feature vector. SVM or XGBoost was trained using the feature vector and its corresponding label. Tree Parzen Estimator (TPE) was used as Bayesian optimization for parameters of SVM and XGBoost. Random search was done for comparison with TPE. Leave-one-out cross-validation was used for optimizing and evaluating the performance of our CADx system. Performance was evaluated using area under the curve (AUC) of receiver operating characteristic analysis. AUC was calculated 10 times, and its average was obtained. The best averaged AUC of SVM and XGBoost was 0.850 and 0.896, respectively; both were obtained using TPE. XGBoost was generally superior to SVM. Optimal parameters for achieving high AUC were obtained with fewer numbers of trials when using TPE, compared with random search. Bayesian optimization of SVM and XGBoost parameters was more efficient than random search. Based on observer study, AUC values of two board-certified radiologists were 0.898 and 0.822. The results show that diagnostic accuracy of our CADx system was comparable to that of radiologists with respect to classifying lung nodules.

Radial mean local binary pattern for noisy texture classification

Local Binary Pattern (LBP) is one of the best descriptors of texture images; however, it elicits information from the pixels’ value over each locality and therefore its value is highly sensitive to additive noise. In this research, a robust-to-noise LBP version is proposed, termed Radial Mean Local Binary Pattern (RMLBP), to enhance the quality of extracted features in noisy images. The main trick of RMLBP is to define the mean of points over each radial instead of using angular neighbor points (over a circle). This changing strategy enables RMLBP to extract robust features by removing the effect of noisy neighbors over each radial local patch. To make a fair comparison, the proposed method along with known mean filters, including circular and square mean, were applied to noisy textures. Applying RMLBP and the compared LBP variants to the Outex, CUReT and UIUC datasets demonstrated a significant superiority of the proposed method to its counterparts.

Jumping and Refined Local Pattern for Texture Classification

The local binary pattern (LBP) model is a simple and effective method of texture classification, but it is sensitive to rotational and noisy images. Although many variants of LBP are proposed by scholars, there are still several urgent problems, such as poor noise and rotation immunity. In this paper, we propose a robust texture descriptor, jumping and refined local pattern (JRLP) for texture classification. In particular, we first extract jumping local difference count pattern (JLDCP) consisting of second-order difference count pattern and diagonal difference count pattern to represent the jumping information in a local domain. To capture the detail information left by JLDCP, we extract a refined completed LBP (RCLBP). By concatenating the JLDCP and RCLBP, we build a JRLP-based robust texture descriptor for classification. Experimental results on four representative texture databases (Brodatz, CUReT, UIUC, and VisTex) reveal that our proposed texture classification method is effective and robust for noise, rotation, scale, and illumination variants and outperforms six representative methods.