Completed Local Binary Pattern 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.

FSDF: A high-performance fire detection framework

Fire detection is crucial in the protection of human life and property. Traditional methodologies and deep learning techniques have been extensively employed in this area, yet they often fall short due to the considerable variability in the shape, size, and intensity of flames. Conventional approaches lean on predefined fire characteristics, failing to adapt sufficiently across a broad spectrum of fire conditions. Simultaneously, deep learning techniques may struggle with managing diverse and unusual flame attributes. In response to these challenges, we introduce a novel framework that synergizes traditional methods with deep learning techniques—the Fire Segmentation-Detection Framework (FSDF). FSDF enhances flame feature detection by extracting color and texture information from images, utilizing Hue, Saturation, and Value (HSV), and the Complete Local Binary Pattern (CLBP). In addition, we weave YOLOv8 and Vector Quantized Variational Autoencoders (VQ-VAE) into the fabric of our framework to facilitate image segmentation and carry out unsupervised fire detection, respectively. To gauge the accuracy and robustness of our proposed method, we implemented a comprehensive assessment using a dataset constructed from real-world forest and urban fires. Experimental results unequivocally demonstrate the competitive edge of our approach over some baseline methods. For instance, in contrast to YOLOv8, our framework has bolstered precision, recall, and F-score by 19.5%, 1.2%, and 11.7% respectively. Finally, we conducted extensive field tests by deploying a robot with the relevant algorithm in an actual fire scenario, further emphasizing our dedication to real-world application. These experiments underline not only the performance of the method, but also its potential for practical deployment.

A Fine-Tuned Hybrid Stacked CNN to Improve Bengali Handwritten Digit Recognition

Recognition of Bengali handwritten digits has several unique challenges, including the variation in writing styles, the different shapes and sizes of digits, the varying levels of noise, and the distortion in the images. Despite significant improvements, there is still room for further improvement in the recognition rate. By building datasets and developing models, researchers can advance state-of-the-art support, which can have important implications for various domains. In this paper, we introduce a new dataset of 5440 handwritten Bengali digit images acquired from a Bangladeshi University that is now publicly available. Both conventional machine learning and CNN models were used to evaluate the task. To begin, we scrutinized the results of the ML model used after integrating three image feature descriptors, namely Binary Pattern (LBP), Complete Local Binary Pattern (CLBP), and Histogram of Oriented Gradients (HOG), using principal component analysis (PCA), which explained 95% of the variation in these descriptors. Then, via a fine-tuning approach, we designed three customized CNN models and their stack to recognize Bengali handwritten digits. On handcrafted image features, the XGBoost classifier achieved the best accuracy at 85.29%, an ROC AUC score of 98.67%, and precision, recall, and F1 scores ranging from 85.08% to 85.18%, indicating that there was still room for improvement. On our own data, the proposed customized CNN models and their stack model surpassed all other models, reaching a 99.66% training accuracy and a 97.57% testing accuracy. In addition, to robustify our proposed CNN model, we used another dataset of Bengali handwritten digits obtained from the Kaggle repository. Our stack CNN model provided remarkable performance. It obtained a training accuracy of 99.26% and an almost equally remarkable testing accuracy of 96.14%. Without any rigorous image preprocessing, fewer epochs, and less computation time, our proposed CNN model performed the best and proved the most resilient throughout all of the datasets, which solidified its position at the forefront of the field.

A multi-channel framework based Local Binary Pattern with two novel local feature descriptors for texture classification

Local binary pattern (LBP) has been widely used in various application fields, including object detection, texture analysis, and remote sensing. To improve discrimination performance, many LBP-based methods have been proposed for extracting different local feature information for texture classification. However, current LBP-based algorithms typically describe local features at a single sampling scale, but some significant and discriminative texture feature information is contained between different scales. Therefore, the lack of capability to extract cross-scale features can lead to losing critical texture features. Moreover, low-frequency texture information should be accorded great importance in texture classification. Additionally, if the diversity and validity of local feature extraction are lacking, the effectiveness of classification capability suffers. To address these issues, (1) we propose a completed cross-scale Local binary pattern (ccsLBP) operator to extract cross-scale texture features. (2) A mean-filtered Local binary pattern (LBPmf) operator is presented to highlight the important low-frequency texture information of texture images. (3) We build a high-performing multi-channel framework based Local binary pattern (MC-LBP) for texture classification, which combines complementary features extracted by LBP, ccsLBP, and LBPmf hybridly to form a final feature vector of the texture image. The effectiveness of the proposed MC-LBP framework is verified on 6 representative texture databases, and the experimental results demonstrate its state-of-art texture classification performance.

Content‐based image retrieval using a fusion of global and local features

AbstractColor, texture, and shape act as important information for images in human recognition. For content‐based image retrieval, many studies have combined color, texture, and shape features to improve the retrieval performance. However, there have not been many powerful methods for combining all color, texture, and shape features. This study proposes a content‐based image retrieval method that uses the combined local and global features of color, texture, and shape. The color features are extracted from the color autocorrelogram; the texture features are extracted from the magnitude of a complete local binary pattern and the Gabor local correlation revealing local image characteristics; and the shape features are extracted from singular value decomposition that reflects global image characteristics. In this work, an experiment is performed to compare the proposed method with those that use our partial features and some existing techniques. The results show an average precision that is 19.60% higher than those of existing methods and 9.09% higher than those of recent ones. In conclusion, our proposed method is superior over other methods in terms of retrieval performance.

CLBP Texture Descriptor in Multipartite Complex Network Configuration for Music Genre Classification

Music genres define the characteristics that musical pieces must have to belong to a given class. These characteristics are reflected in the audio signal and, consequently, in the image that represents its spectral content: the spectrogram. In this paper, we propose a Music Genre Classification (MGC) system based on representation with complex networks of CLBP (Completed Local Binary Pattern) texture descriptor codes extracted from spectrograms: mel-spectrogram and gammatonegram. Complex networks were generated using CLBP codes in multipartite configuration: mono, bi, and tripartite networks; where the three node types are signal (CLBP-S), magnitude (CLBP-M) and central (CLBP-C) codes. The networks were mined using conventional, textural, and multipartite topological measures. In order to test the proposed MGC, we used the GTZAN dataset and defined several experiments using combinations of multipartite measures: 1) monopartite, 2) mono and bipartite, and 3) mono, bi and tripartite. All experiments were performed for each spectrogram individually and jointly. In the machine learning stage, we used the ensemble classifier Bagging with Random Forest, and 10-fold cross-validation repeated 100 times. As a main result, it was found that the bipartite measures related to CLBP-C decrease the performance, but the tripartites increased it. Moreover, in most experiments using only gammatonogram the performance was better. Consequently, the experiment using tripartite measures extracted from the gammatonegram revealed a satisfactory result, indicating that the proposed MGC is promising.

Coal rock image recognition method based on improved CLBP and receptive field theory

AbstractRapid coal‐rock identification is one of the key technologies for intelligent and unmanned coal mining. Currently, the existing image recognition algorithms cannot satisfy practical needs in terms of recognition speed and accuracy. In view of the evident differences between coal and rock in visual attributes such as color, gloss and texture, the complete local binary pattern (CLBP) image feature descriptor is introduced for coal and rock image recognition. Given that the original algorithm oversimplifies local texture features by ignoring imaging information from higher‐order pixels and the concave and convex areas between adjacent sampling points, this paper proposes a higher‐order differential median CLBP image feature descriptor to replace the original CLBP center pixel gray with a local gray median, and replace the binary differential with a second‐order differential. Meanwhile, for the high dimensionality of CLBP descriptor histogram and feature redundancy, deep learning perceptual field theory is introduced to realize data nonlinear dimensionality reduction and deep feature extraction. With relevant experiments conducted, the following conclusion can be drawn: (1) Compared with that of the original CLBP, the recognition accuracy of the improved CLBP algorithm is greatly improved and finally stabilized above 94.3% under strong noise interference; (2) Compared with that of the original CLBP model, the single image recognition time of the coal rock image recognition model fusing the improved CLBP and the receptive field theory is 0.0035 s, a reduction of 71.0%; compared with the improved CLBP model (without the fusion of receptive field theory), it can shorten the recognition time by 97.0%, but the accuracy rate still maintains more than 98.5%. The method offers a valuable technical reference for the fields of mineral development and deep mining.

Neutrosophic set based local binary pattern for texture classification

This paper presents novel neutrosophic set-based Completed Local Binary Pattern (CLBP) hybrid methods. These methods first transform the input image into a neutrosophic domain, and the image's texture is characterized by truth and false membership components. Grayscale images are more robust to noise in the neutrosophic domain. The neutrosophic components suppress noise, by this way edges in the images may be detected more precisely. As a result, using neutrosophic truth and false sets with a grayscale input image has resulted in more robust features. Besides, these discriminative features are combined with rotation invariant LBP features with a cross-scale joint coding strategy. The proposed methods can contribute to the classification performance with a reasonable computational cost. When compared to state-of-the-art hand-crafted approaches, experimental findings on state-of-the-art texture databases show that the proposed methods can enhance classification accuracy by roughly 24%. Moreover, the proposed method achieves up to 34% better results than the state-of-the-art deep learning-based methods.

Egg Pattern Mimicry in Avian Brood Parasitism Assessed Using Local Image Descriptors and Human-Eyes

Egg pattern plays a critical role in avian brood parasitism where a brood parasite exploits a host's parental care by laying eggs in host nests relying on the host to rear the parasitic egg(s). It has been demonstrated that many hosts have evolved an ability to recognize and reject parasitic eggs, based on differences in egg patterns, as a defense against brood parasitism, while some parasites have evolved egg mimicry to counter the host defense. Egg pattern is a complex phenotype and its quantitative evaluation has been a focus of much research. In this paper, we propose a novel method for quantifying an egg pattern and assessing the degree of egg pattern mimicry—a measure of the similarity of a parasite egg to host eggs. Our approach is based on image analysis focused on local binary pattern (LBP) and its variant completed local binary pattern (CLBP) that captures the local structure of a pattern in an image. We compare the results obtained by LBP and CLBP with human-eye evaluation, a classical method widely used in previous studies. Both LBP and CLBP can successfully assess the similarity in egg pattern that is positively correlated with human-eye scores with a high accuracy rate. LBP tends to perform better than CLBP at small scales while CLBP performs better for a wide range of larger scales. Our method can be an effective alternative means of assessing the degree of egg pattern mimicry objectively, providing a useful tool for biologists studying avian brood parasitism. Many bird species have specific eggshell patterns (e.g. spots, blotches, lines) and it has been suggested that these patterns are an important functional trait, such as for camouflage against predators. We propose our method as a useful and objective tool for assessing egg patterns.

Texture image classification based on a pseudo-parabolic diffusion model.

This work proposes a novel method based on a pseudo-parabolic diffusion process to be employed for texture recognition. The proposed operator is applied over a range of time scales giving rise to a family of images transformed by nonlinear filters. Therefore each of those images are encoded by a local descriptor (we use local binary patterns for that purpose) and they are summarized by a simple histogram, yielding in this way the image feature vector. Three main novelties are presented in this manuscript: (1) The introduction of a pseudo-parabolic model associated with the signal component of binary patterns to the process of texture recognition and a real-world application to the problem of identifying plant species based on the leaf surface image. (2) We also introduce a simple and efficient discrete pseudo-parabolic differential operator based on finite differences as texture descriptors. While the work in [26] uses complete local binary patterns, here we use the original version of the local binary pattern operator. (3) We also discuss, in more general terms, the possibilities of exploring pseudo-parabolic models for image analysis as they balance two types of processing that are fundamental for pattern recognition, i.e., they smooth undesirable details (possibly noise) at the same time that highlight relevant borders and discontinuities anisotropically. Besides the practical application, the proposed approach is also tested on the classification of well established benchmark texture databases. In both cases, it is compared with several state-of-the-art methodologies employed for texture recognition. Our proposal outperforms those methods in terms of classification accuracy, confirming its competitiveness. The good performance can be justified to a large extent by the ability of the pseudo-parabolic operator to smooth possibly noisy details inside homogeneous regions of the image at the same time that it preserves discontinuities that convey critical information for the object description. Such results also confirm that model-based approaches like the proposed one can still be competitive with the omnipresent learning-based approaches, especially when the user does not have access to a powerful computational structure and a large amount of labeled data for training.

Meta-heuristic-based FCM-UNet segmentation with multi-objective function and deep learning for brain tumour classification

ABSTRACT A technique of automated segmentation has been introduced in this paper, which makes the tumour segmentation out of MRI images, apart from improving the effectiveness of segmentation as well as classification. Once the dataset of MRI dataset is collected from different public sources, the pre-processing of the image is performed by the median filtering and contrast enhancement. The segmentation of brain tumour is the main contribution of this paper, which concentrates on developing the Adaptive Influence Factor-based Elephant Herding Optimisation (AIF-EHO)-based FCM-UNet fusion segmentation with multi-objective function. Then, the feature extraction is performed using Completed Local Binary Pattern (CLBP) and Local Gradient Pattern (LGP). These extracted features are further used in deep learning using Enhanced Recurrent Neural Network (RNN) for brain tumour classification. Results demonstrated on public benchmarks described that this method attains competitive accuracy than the conventional techniques while being computationally effective.

COMPLETED LOCAL BINARY PATTERN WITH RANDOM FOREST ON IMAGE CLASSIFICATION

The work develops effective machine learning models for image classification. The work also discusses the performance of random forest with completed local binary pattern feature extraction. Texture feature extraction methods, such as completed local Binary Pattern, can be used to address classification problems successfully

Breast cancer histopathological images recognition based on two-stage nuclei segmentation strategy.

Pathological examination is the gold standard for breast cancer diagnosis. The recognition of histopathological images of breast cancer has attracted a lot of attention in the field of medical image processing. In this paper, on the base of the Bioimaging 2015 dataset, a two-stage nuclei segmentation strategy, that is, a method of watershed segmentation based on histopathological images after stain separation, is proposed to make the dataset recognized to be the carcinoma and non-carcinoma recognition. Firstly, stain separation is performed on breast cancer histopathological images. Then the marker-based watershed segmentation method is used for images obtained from stain separation to achieve the nuclei segmentation target. Next, the completed local binary pattern is used to extract texture features from the nuclei regions (images after nuclei segmentation), and color features were extracted by using the color auto-correlation method on the stain-separated images. Finally, the two kinds of features were fused and the support vector machine was used for carcinoma and non-carcinoma recognition. The experimental results show that the two-stage nuclei segmentation strategy proposed in this paper has significant advantages in the recognition of carcinoma and non-carcinoma on breast cancer histopathological images, and the recognition accuracy arrives at 91.67%. The proposed method is also applied to the ICIAR 2018 dataset to realize the automatic recognition of carcinoma and non-carcinoma, and the recognition accuracy arrives at 92.50%.

Completed local binary patterns feature integrated convolutional neural network-based terrain classification algorithm in polarimetric synthetic aperture radar images

We propose a polarimetric synthetic aperture radar (PolSAR) image terrain classification algorithm based on complete local binary patterns (CLBP) feature integrated convolutional neural network (CNN) (CLBP-CNN). Traditional CNN has a powerful high-level deep features extraction ability, which can effectively improve the terrain classification accuracy in PolSAR images. However, most traditional CNN-based methods only focus on the high-level deep feature extraction of the synthetic aperture radar (SAR) terrains; they ignore the low-level texture features, resulting in incomplete feature representation and poor classification accuracy. In fact, low-level texture features also play an important role in PolSAR terrain classification. To solve the problem that traditional CNN-based terrain classification methods easily lose the low-level texture features in the process of feature extraction, the proposed method uses the CLBP descriptor to extract multi-level texture features under different receptive fields, and it adaptively combines the high-level deep features and the low-level texture features for better SAR terrain feature description. CLBP-CNN greatly alleviates the shortcomings of traditional CNN in missing the low-level texture features; it improves the feature representation completeness, so it can achieve better terrain classification results. The superiority of CLBP-CNN is verified on the data sets of Flevoland, San Francisco, and Oberpfaffenhofen.

Classification of breast cancer images using completed local ternary pattern and support vector machine

Breast cancer is the major cause of deaths in women compared to other cancers. Though early detection of breast cancer reduces cancer deaths, it is a challenging task for physicians. Local binary pattern (LBP) and local ternary pattern (LTP) techniques are widely applied in texture classification applications. Since LBP is more sensitive to noise in texture classification, it needs to be improved for achieving better results. Though LTP is more robust to noise, there are few drawbacks. Completed LBP and completed local binary count techniques achieve good accuracy for texture classification, but they inherit few drawbacks of LBP. In this paper, completed LTP operator is applied on breast cancer images for better classification accuracy than LBP and completed LBP operators, by extracting sign and magnitude components. Experimental results based on breast cancer database show that the proposed technique achieved better classification accuracy than existing similar approaches.

A Robust Machine Learning Approach for Multiclass Alzheimer’s Disease Detection using 3D Brain Magnetic Resonance Images

Alzheimer’s disease (AD), a progressive dementia is the neurodegenerative disorder that worsens memory and mental capabilities mostly in aged people. Currently, clinical and psychometric assessments are being used to diagnose the disease in patients. In clinical procedures, 3D Magnetic Resonance Image qualitative parameters are analyzed to identify the abnormality in brain shape, volume, texture, and cortical thickness. This paper presents a robust approach for categorizing 3D MR images into multiple stages of AD using hybrid features viz., Gray Level Co-occurrence Matrix (GLCM), 3D Scale and rotation Invariant Feature Transform (3D SIFT), Histogram of Oriented Gradients – Three Orthogonal Planes (HOG-TOP) and Complete Local Binary Pattern of Sign and Magnitude – Three Orthogonal Planes (CLBPSM-TOP). The proposed algorithm is validated using Open Access Series of Imaging Studies (OASIS) datasets to classify the subjects into AD, Mild Cognitive Impairment (MCI) and Cognitive Normal (CN) categories using various classifiers. Moreover, this approach is also evaluated and compared with the state-of-the-art approaches. 87.84% diagnosis accuracy is achieved with Ensemble classifier using hybrid features to diagnose the severity of AD. This approach also outperforms majority of these techniques in key parameters viz., accuracy, precision, recall and F1-score.

Bag of Features (BoF) Based Deep Learning Framework for Bleached Corals Detection

Coral reefs are the sub-aqueous calcium carbonate structures collected by the invertebrates known as corals. The charm and beauty of coral reefs attract tourists, and they play a vital role in preserving biodiversity, ceasing coastal erosion, and promoting business trade. However, they are declining because of over-exploitation, damaging fishery, marine pollution, and global climate changes. Also, coral reefs help treat human immune-deficiency virus (HIV), heart disease, and coastal erosion. The corals of Australia’s great barrier reef have started bleaching due to the ocean acidification, and global warming, which is an alarming threat to the earth’s ecosystem. Many techniques have been developed to address such issues. However, each method has a limitation due to the low resolution of images, diverse weather conditions, etc. In this paper, we propose a bag of features (BoF) based approach that can detect and localize the bleached corals before the safety measures are applied. The dataset contains images of bleached and unbleached corals, and various kernels are used to support the vector machine so that extracted features can be classified. The accuracy of handcrafted descriptors and deep convolutional neural networks is analyzed and provided in detail with comparison to the current method. Various handcrafted descriptors like local binary pattern, a histogram of an oriented gradient, locally encoded transform feature histogram, gray level co-occurrence matrix, and completed joint scale local binary pattern are used for feature extraction. Specific deep convolutional neural networks such as AlexNet, GoogLeNet, VGG-19, ResNet-50, Inception v3, and CoralNet are being used for feature extraction. From experimental analysis and results, the proposed technique outperforms in comparison to the current state-of-the-art methods. The proposed technique achieves 99.08% accuracy with a classification error of 0.92%. A novel bleached coral positioning algorithm is also proposed to locate bleached corals in the coral reef images.

Trustworthy authorization method for security in Industrial Internet of Things

Industrial Internet of Things (IIoT) realizes machine-to-machine communication and human–computer interaction (HCI) through communication network, which makes industrial production automatic and intelligent. Security is critical in IIoT because of the interconnection of intelligent industrial equipment. In IIoT environment, legitimate human–computer interaction can only be performed by authorized professionals, and unauthorized access is not tolerated. In this paper, a reliable authentication method based on biological information is proposed. Specifically, the complete local binary pattern (CLPB) and the statistical local binary pattern (SLPB) are introduced to describe the local vein texture characteristics. Meanwhile, the contrast energy and frequency domain information are regarded as auxiliary information to interpret the finger vein. The distance between the features of the registration image and the test image is used to recognize the finger vein image, so as to realize identity authentication. The experiments are carried out on SDUMLA-FV database and FV-USM database, and results show that the presented method has achieved high recognition accuracy.

Breast Cancer Histopathological Images Recognition Based on Low Dimensional Three-Channel Features.

Breast cancer (BC) is the primary threat to women’s health, and early diagnosis of breast cancer is imperative. Although there are many ways to diagnose breast cancer, the gold standard is still pathological examination. In this paper, a low dimensional three-channel features based breast cancer histopathological images recognition method is proposed to achieve fast and accurate breast cancer benign and malignant recognition. Three-channel features of 10 descriptors were extracted, which are gray level co-occurrence matrix on one direction (GLCM1), gray level co-occurrence matrix on four directions (GLCM4), average pixel value of each channel (APVEC), Hu invariant moment (HIM), wavelet features, Tamura, completed local binary pattern (CLBP), local binary pattern (LBP), Gabor, histogram of oriented gradient (Hog), respectively. Then support vector machine (SVM) was used to assess their performance. Experiments on BreaKHis dataset show that GLCM1, GLCM4 and APVEC achieved the recognition accuracy of 90.2%-94.97% at the image level and 89.18%-94.24% at the patient level, which is better than many state-of-the-art methods, including many deep learning frameworks. The experimental results show that the breast cancer recognition based on high dimensional features will increase the recognition time, but the recognition accuracy is not greatly improved. Three-channel features will enhance the recognizability of the image, so as to achieve higher recognition accuracy than gray-level features.

Intensity prediction of tropical cyclone using multilayer multi-block local binary pattern and tree-based classifiers over North Indian Ocean

Local binary pattern (LBP) is an extensively used method in image analysis and pattern recognition related works across various domains. In this paper, we explore cyclone cloud evolution patterns using a modified LBP. The multilayer multi-block local binary pattern (MMLBP) is an extended version of Completed LBP (CLBP) with multiple blocks and many layers. A tropical cyclone (TC) image is converted into 3 × 3 identical blocks to generate central pixels using CLBP magnitude descriptor block-wise (CLBP_MB). These central pixels are used to find the next layer, which is further divided into 3 × 3 blocks to generate central pixels by CLBP_MB descriptor. This technique will iterate until the dimension of the final layer reached to 1 × 1. These central pixels are extracted from each layer and gather in a vector called a feature vector. The proposed method is applied to 600 tropical cyclone (TC) images and each image generates one feature vector. Hence, 600 vectors are passed through tree-based classifiers to classify cyclone images of various classes. The proposed method of feature extraction and classification reaches a maximum of 84.66% accuracy using Random Forest (RF) classifier. The root mean squared error (RMSE) of the MMLBP method is 9.27 kt, which is better than the LBP and MBLBP approaches. Finally, the size of the original feature vector is reduced to 97.4% using correlation based feature subset selection method.