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Classification of wool and cashmere fiber based on LBP and DWT features: performance comparison of random forest, AdaBoost, and KNN classifiers

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JOURNAL OF THE TEXTILE INSTITUTE 2025年

作者： Fang, Xinyue Xin, Binjie Zhan, Zhu Newton, Md All Amin Yan, Feng Jin, Zhenhui Shanghai Univ Engn Sci Sch Text & Fash Art Bldg333 Longteng Rd Shanghai 201620 Peoples R China

The automatic identification of cashmere and wool fibers presents a significant challenge due to their substantial structural and textural similarities. An image-based method is proposed in this article, which combines local binary pattern (LBP), discrete wavelet transform (DWT), and several classification algorithms - including random forest (RF), adaptive boosting (AdaBoost), and K-nearest neighbors (KNN) - to achieve rapid and accurate differentiation between wool and cashmere fibers. Primarily, 300 images each of cashmere and wool fibers were captured using optical microscopy, and the original images were pre-processed to prepare input data for feature extraction. Texture features were then extracted using LBP, and energy entropy was extracted using DWT. Fiber diameters were measured through geometric morphological analysis, using the maximum inscribed circle method to determine fiber diameter from segmented images. In conclusion, these features were input into RF, AdaBoost, and KNN classifiers for model training and classification. Experimental results indicate that the RF classifier achieved superior performance, with a classification accuracy of 94.88%, outperforming both AdaBoost and KNN, and demonstrating high recall, and F1 scores. Overall, the proposed method exhibits exceptional accuracy, robustness, and generalization capability in fiber classification tasks, with significant potential for application to the classification of other natural fibers.

关键词： Fiber identification local binary patterns discrete wavelet transform random forests

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Fractional-order gradient based local binary pattern for texture classification

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COMPUTERS & ELECTRICAL ENGINEERING 2025年 124卷

作者： Alpaslan, Nuh Hanbay, Kazim Bingol Univ Sch Engn & Architecture Dept Comp Sci Bingol Turkiye Inonu Univ Sch Engn Dept Software Engn Malatya Turkiye

The local binary patterns method plays an efficient role in texture classification and feature extraction. These approaches extract textural features by using the neighboring pixel values. The single or joint histogram of the texture image is constructed from the LBP features obtained from local relationships. In this study, a method of utilizing fractional derivative information effectively has been proposed for classifying color texture images. The magnitude of the fractional horizontal and vertical derivatives obtained with Gaussian derivative filters are integrated into the ACS-LBP method. The magnitude information of the fractional derivatives of local texture patterns has been modeled according to the relationship between neighboring pixels. The computed derivative information has been incorporated into the ACS-LBP model to effectively encode the local pixel relationship. In order to maintain, these fractional-order edge and texture transition detection operators provide both high robustness and continue to detect small textural details. To accomplish these capabilities, the fractional-order parameter is tuned to target particular pixel transition frequencies. This gives the proposed LBP method greater latitude in selecting the fractional-order mask. An additional degree of freedom in designing various masks is provided by the fractional-order parameter. The developed model has been evaluated on widely used texture databases. It also has been compared with existing LBP and deep learning models in terms of different performance metrics. The proposed method has shown significant advantages over up to date methods in both classification accuracy and execution time.

关键词： local binary patterns Fractional derivative Texture classification

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A Noise Invariant Method for Bearing Fault Detection and Diagnosis Using Adapted local binary Pattern (ALBP) and Short-Time Fourier Transform (STFT)

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IEEE ACCESS 2024年 12卷 107247-107260页

作者： Geraei, Hosna Rodriguez, Edgar Armando Vazquez Majma, Ehsan Habibi, Saeid Al-Ani, Dhafar McMaster Univ Dept Mech Engn Hamilton L8S 4L8 ON Canada McMaster Univ Dept Elect & Comp Engn Hamilton L8S 4L8 ON Canada

This study proposes a new method for bearing Fault Detection and Diagnosis (FDD) in Belt Starter Generators (BSGs) using vibration signals. The Adapted local binary Pattern (ALBP) method is introduced, and its performance is compared to the conventional local binary Pattern (LBP) technique and a Convolutional Neural Network with Support Vector Machine (CNN-SVM) model. Significantly, ALBP demonstrates superior accuracy without significantly increasing computational complexity, outperforming both LBP and the CNN-SVM model. The experimental setup involves a Specialty Motor Testing system, with vibration data collected using a single accelerometer under specific speed and torque conditions. The focus is on detecting and diagnosing bearing faults, such as lubrication and contamination, under various test conditions for Original Equipment Manufacturer (OEM) and After-Market (AM) bearings. ALBP achieves diagnostic accuracy ranging from 99% to 99.8%, representing a significant advancement in bearing FDD. Another novel aspect of this study is the training and testing of the model on separate days. This approach ensures the model's robustness against data variability and domain shifts, unlike the traditional random data splitting method, which can yield misleadingly high accuracy on a portion of data but fails to generalize. Results show that ALBP achieves an average diagnosis accuracy of 99.4%, compared to 80% for the CNN-SVM model, further highlighting the superior performance of ALBP.

关键词： Vibrations Testing Convolutional neural networks Accuracy Fault detection Support vector machines Induction motors Bearing faults fault detection and diagnosis local binary patterns machine learning noise signal processing short-time Fourier transform

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SISIM: statistical information similarity-based point cloud quality assessment

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VISUAL COMPUTER 2025年第1期41卷 625-638页

作者： Xiao, Shuyu Wang, Yongfang Wang, Yihan Shanghai Univ Shanghai Inst Adv Commun & Data Sci Sch Commun & Informat Engn Shanghai 200444 Peoples R China

Due to the widespread use of point cloud, the demand for compression and transmission is more and more prominent. However, this cause various losses to the point cloud. It is necessary for application to evaluate the quality of point cloud. Therefore, we propose a new point cloud quality assessment (PCQA) metric named statistical information similarity (SISIM). First, we preprocess point cloud (PC) by scaling based on density and then project PC into texture maps and geometry maps. In addition, the SISIM based on Natural Scene Statistics (NSS) is proposed as texture features under the premise of proving that the texture maps meet NSS. Furthermore, we propose to extract geometry features based on local binary patterns (LBP) on account of the phenomenon that LBP maps of geometry images vary with different distortions. Finally, we predict the quality of PCs by fusing texture features with geometry features. Experiments show that our proposed method outperforms the state-of-the-art PCQA metrics on three publicly available datasets.

关键词： local binary patterns Point cloud quality assessment Statistical similarity Support vector regression

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Tea leaf disease recognition using attention convolutional neural network and handcrafted features

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CROP PROTECTION 2025年 190卷

作者： Wu, Peng Liu, Jinlan Jiang, Mingfu Zhang, Li Ding, Shining Zhang, Kewang Xinyang Agr & Forestry Univ Sch Informat Engn Xinyang 464000 Peoples R China Macao Polytech Univ Fac Appl Sci Macau 999078 Peoples R China

The diseases of tea leaves have a significant impact on their quality and yield, making the rapid identification of leaf diseases in tea crucial for prevention and control. We propose an LBPAttNet model, incorporating a lightweight coordinate attention mechanism into ResNet18 to enhance disease localization and reduce background interference. Furthermore, we employ the local binary patterns (LBP) algorithm to further extract local structural and textural features of tea leaf diseases, and integrate deep features to obtain a more comprehensive feature representation. Additionally, we utilize the focal loss function to alleviate the issues of class imbalance and varying difficulty levels in tea leaf disease, thereby further enhancing the accuracy of tea disease recognition. Our model achieves an accuracy of 92.78% and 98.13% on two publicly available tea disease datasets, surpassing ResNet18 by 3.84% and 2.59% respectively. Compared to traditional algorithms such as AlexNet, GoogleNet, MobileNet, VGG16, and other tea disease recognition algorithms, our model also shows significant improvements. These results highlight the superior performance and robustness of our model.

关键词： Tea leaf disease Deep learning Attention local binary patterns Handcrafted features

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Deep learning vs. adversarial noise: a battle in malware image analysis

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第7期27卷 9191-9220页

作者： Asmitha, K. A. Puthuvath, Vinod Rehiman, K. A. Rafidha Ananth, S. L. Cochin Univ Sci & Technol Dept Comp Applicat Kochi 682022 Kerala India Univ Padua Dept Math Padua Italy Cisco Syst India Pvt Ltd Bangalore India

The proliferation of malware variants has shown a steep increase, attributed to their enhanced sophistication and the utilization of the latest technologies. This constitutes a severe menace to smart gadgets and IT infrastructure. Malware visualization has emerged as an exceptionally attractive technique, primarily because it obviates the need for disassembly or code execution. In this approach, malicious executables are transformed into visual representations resembling images. This visual representation allows for the extraction of textural features using the local binary Pattern (LBP) technique. Subsequently, classification models are constructed using ResNet50, VGG16, and customized models tailored to the specific task. These model undergoes extensive evaluation through two benchmark datasets: the MalImg dataset (consisting of 9,342 instances of malware across 25 families) and the Malware Classification Challenge dataset (BIG2015) (with 10,868 labeled malware instances across nine families). Additionally, the model is validated on a self-made dataset, which we named Malhub, consisting of 26,452 executables comprising 20 families. Furthermore, we implemented a white-box adversarial attack using additive noise (Gaussian, local Variable, Poisson, Salt and Pepper, Speckle). We observed an F1 score in the range of 0.992-\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$-$$\end{document}0.993 for MalImg, 0.874-\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$-$$\end{document}0.878 for BIG2015, and 0.014-\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssym

关键词： Malware classification Malware visualization Deep learning Adversarial attack local binary patterns Classifier fusion

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Tex-Net: texture-based parallel branch cross-attention generalized robust Deepfake detector

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MULTIMEDIA SYSTEMS 2024年第4期30卷 233-233页

作者： Dagar, Deepak Vishwakarma, Dinesh Kumar Delhi Technol Univ Biometr Res Lab Dept Informat Technol Bawana Rd Delhi 110042 India

In recent years, artificial faces generated using Generative Adversarial Networks (GANs) and Variational Auto-encoders (VAEs) have become more lifelike and difficult for humans to distinguish. Deepfake refers to highly realistic and impressive media generated using deep learning technology. Convolutional Neural Networks (CNNs) have demonstrated significant potential in computer vision applications, particularly identifying fraudulent faces. However, if these networks are trained on insufficient data, they cannot effectively apply their knowledge to unfamiliar datasets, as they are susceptible to inherent biases in their learning process, such as translation, equivariance, and localization. The attention mechanism of vision transformers has effectively resolved these limits, leading to their growing popularity in recent years. This work introduces a novel module for extracting global texture information and a model that combines data from CNN (ResNet-18) and cross-attention vision transformers. The model takes in input and generates the global texture by utilizing Gram matrices and local binary patterns at each down sampling step of the ResNet-18 architecture. The ResNet-18 main branch and global texture module operate simultaneously before inputting into the visual transformer's dual branch's cross-attention mechanism. Initially, the empirical investigation demonstrates that counterfeit images typically display more uniform textures that are inconsistent across long distances. The model's performance on the cross-forgery dataset is demonstrated by experiments conducted on various types of GAN images and Faceforensics + + categories. The results show that the model outperforms the scores of many state-of-the-art techniques, achieving an accuracy score of up to 85%. Furthermore, multiple tests are performed on different data samples (FF + +, DFDCPreview, Celeb-Df) that undergo post-processing techniques, including compression, noise addition, and blurring. These studi

关键词： Deepfake detector local binary patterns Texture Gram matrices Generalization Robustness

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Light Field Image Quality Assessment Using Natural Scene Statistics and Texture Degradation

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2024年第3期34卷 1696-1711页

作者： Ma, Jian Zhang, Xiaoyin Jin, Cheng An, Ping Xu, Guoming Fudan Univ Sch Comp Sci Shanghai 200433 Peoples R China Anhui Univ Sch Internet Hefei 230039 Peoples R China Shanghai Univ Sch Commun & Informat Engn Shanghai 200444 Peoples R China

Light field image (LFI) now is becoming increasingly popular in immersive media applications. Unlike traditional 2D and 3D images, images taken by light field cameras can capture both angular and spatial information. However, the spatial and angular information of LFI is highly inter-twined with varying disparities, which poses a higher challenge to the quality assessment of LFI. To address this issue, this paper proposes a full-reference light field image quality assessment (LFIQA) index that attempts to disentangle the coupling information from macro-pixel image (MacPI) to accurately evaluate the entire LFI quality. The proposed framework can be divided into three steps. Firstly, the LFIs are converted into the MacPIs, and then the spatial and angular feature maps are disentangled by using the spatial, angular and epipolar plane image (EPI) convolutions in the MacPI mode. Secondly, the structural similarity (SSIM) maps are calculated between the disentangled feature maps of the original and distorted LFIs. Furthermore, the quality-aware features of LFIs are extracted on the SSIM maps by utilized local binary patterns (LBP) and natural scene statistics (NSS). Finally, support vector regression (SVR) is utilized to predict the qualities of LFIs. Extensive experiments show that the proposed model outperforms multiple classical and state-of-the-art methods.

关键词： Light field image image quality assessment macro-pixel image local binary patterns natural scene statistics support vector regression

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Protection against adversarial attacks with randomization of recognition algorithm

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JOURNAL OF COMPUTER VIROLOGY AND HACKING TECHNIQUES 2024年第1期20卷 127-133页

作者： Marshalko, Grigory Koreshkova, Svetlana Tech Comm Standardisat Cryptog & Secur Mech TC 02 Moscow Russia Higher Sch Econ Moscow Russia JSC Kryptonite Moscow Russia

We study a randomized variant of one type of biometric recognition algorithms, which is intended to mitigate adversarial attacks. We show that the problem of an estimation of the security of the proposed algorithm can be formulated in the form of an estimation of statistical distance between the probability distributions, induced by the initial and the randomized algorithm. A variant of practical password-based implementation is discussed. The results of experimental evaluation are given. The preliminary verison of this research was presented at CTCrypt 2020 workshop.

关键词： Biometric recognition Statistical distance local binary patterns Password based authentication

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Feature selection and mapping of local binary pattern for texture classification

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第5期82卷 7639-7676页

作者： Shakoor, Mohammad Hossein Boostani, Reza Sabeti, Malihe Mohammadi, Mokhtar Arak Univ Fac Engn Dept Comp Engn Arak *** Iran Shiraz Univ Sch Elect & Comp Engn Comp Engn Dept Shiraz Iran Azad Univ Dept Comp Engn North Tehran Branch Tehran Iran

local binary pattern is one of the most known descriptors, which is used for texture classification. Although completed local binary pattern is seemingly the most precise variant of this type of descriptor and provides high classification accuracy by joining three histograms of features. Merging these histograms increases the features number significantly. To reduce the size of features, in this paper, some mapping methods are proposed for feature reduction and mapping of these features into a histogram. All of the proposed mapping methods are rotation and illumination invariant. Furthermore, a constraint feature selection method is proposed that selects discriminative features. Applying the introduced methods to the known benchmarks like Outex (TC3, TC10, TC13, TC12(t) and TC12(h)), UIUC, CUReT and Defect Fabric datasets indicates that even by adopting lower number of features, the classification rate is enhanced from 1% to 9% while the features number are decreased around 10% to 99%. Comparison results on the same datasets imply the superiority of the proposed schemes to the conventional methods.

关键词： Features mapping local binary patterns Features selection Texture classification

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