检索结果-内蒙古大学图书馆

Content-based image retrieval using multi-scale averaging local binary patterns

DIGITAL SIGNAL PROCESSING 2024年 146卷

作者： Fadaei, Sadegh Dehghani, Abbas Ravaei, Bahman Univ Yasuj Dept Elect Engn Fac Engn Yasuj Iran Univ Yasuj Dept Comp Engn Fac Engn Yasuj Iran

Feature extraction has a significant impact on the accuracy of Content -Based Image Retrieval (CBIR) algorithms since the content of images is encoded in feature vectors. In this paper, an effective method for texture feature extraction is proposed based on local patterns. In the proposed method, first the image is formed in different scales, then the texture features are extracted from the scales of the image. Finally, extracted features of different scales are concatenated to construct the final feature vector. To evaluate the proposed method, five datasets including Corel-1k, Brodatz, VisTex, Corel-10k, STex, Caltech256, and Oliva are used. In the evaluation process, the effect of different scales and different coding schemes on retrieval precision is investigated. The proposed method is compared with existing CBIR models based on local patterns. The proposed method achieves the best precision of 64.16%, 81.66%, 88.59%, 30.63%, and 69.63%, 13.59%, and 64.10% on the Corel-1k, Brodatz, VisTex, Corel-10k, STex, Caltech256, and Oliva datasets, respectively.

关键词： Content-based image retrieval local binary pattern Texture features local patterns

来源：评论

学校读者我要写书评

暂无评论

Combining Multi-Layer Perceptron and local binary patterns for Thermite Weld Defects Classification 1st

Combining Multi-Layer Perceptron and Local Binary Patterns ...

引用

1st International Conference on Pan-African Intelligence and Smart Systems, PAAISS 2021

作者： Molefe, Mohale Emmanuel Tapamo, Jules-Raymond University of KwaZulu-Natal Durban South Africa Transnet Freight Rail Johannesburg South Africa

ISBN: (纸本)9783030933135

During the new or existing rail network installation, sections of rails are welded together to produce a continuously welded railway. Thermite welding is the widely used welding method to permanently joint rail sections, and Non-Destructive Testing methods such as radiography are used to inspect the formed weld joint for quality assurance. Conventionally, the process of detecting and classifying defects in the generated radiography images is conducted manually by trained experts. However, the conventional process is lengthy, costly and has a high false alarm rate even if experts conduct it. This work proposes a method based on computer vision algorithms for automatic detection and classification of defects. The proposed method uses local binary patterns as a feature descriptor and Multi-Layer Perceptron as a classifier. The results obtained indicated that the proposed method outperforms the state-of-the-art method found in the literature. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： local binary pattern

来源：评论

学校读者我要写书评

暂无评论

Sorted Uniform local binary patterns 11th

Sorted Uniform Local Binary Patterns

引用

11th International Conference on Robotics, Vision, Signal Processing and Power Applications, RoViSP 2021

作者： Kalyoncu, Cem European University of Lefke Lefke10 Turkey

ISBN: (纸本)9789811681288

Sorted Uniform LBP (SULBP) is a rotation invariant local binary patterns (LBP) variant that is proposed for leaf image identification. This method is simple and effective, and is shown to outperform other LBP variants in leaf recognition. However, comparative analysis of this method in terms of texture classification is limited. In this paper, we present our extensive comparative analysis of SULBP. Additionally, we have explored different parameters of this system and their effect on the performance. Finally, we have used multiple datasets and classifiers to show the validity of these experiments. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： local binary pattern

来源：评论

学校读者我要写书评

暂无评论

News Classification Using local Text pattern and TF-IDF Features 26

News Classification Using Local Text Pattern and TF-IDF Feat...

引用

26th International Conference on Computer and Information Technology, ICCIT 2023

作者： Afroz, Nadia Azim, Muhammad Anwarul Islam, Mohammad Khairul University of Chittagong Department of Computer Science and Engineering Chittagong4331 Bangladesh

ISBN: (纸本)9798350359015

In this modern era, news documents are available in large volumes in online news portals. Based on news contents they can be categorized into different groups e.g., politics, sports, etc. Thus, the automatic classification of news text can provide news of desired categories to the users. In this paper, we propose an approach that aims to categorize a given news text document with high accuracy. Our approach consists of four modules pre-processing, feature Extraction, signature Generation, and classification. Firstly, a news document is pre-processed using a rule-based approach to remove less important words. Term Frequency-Inverse Document Frequency (TF-IDF) is used for extracting statistical features. Then, we extend the concept of the local binary pattern of image processing for the first time in the text classification domain for extracting the local Text pattern feature. We combine these features to generate a signature for each document. Finally, the Support Vector Machine is fine-tuned for training and testing. Our approach uses the BBC news dataset for experiments and obtains superior performance in terms of accuracy of 99.63% over the state-of-the-art approaches. © 2023 IEEE.

关键词： and Image Processing local binary pattern local Text pattern Natural Language Processing News Classification Term Frequency-Inverse Document Frequency

来源：评论

学校读者我要写书评

暂无评论

pACPs-DNN: Predicting anticancer peptides using novel peptide transformation into evolutionary and structure matrix-based images with self-attention deep learning model

引用

COMPUTATIONAL BIOLOGY AND CHEMISTRY 2025年 117卷 108441页

作者： Shahid Hayat, Maqsood Raza, Ali Akbar, Shahid Alghamdi, Wajdi Iqbal, Nadeem Zou, Quan Abdul Wali Khan Univ Mardan Dept Comp Sci Mardan 23200 KP Pakistan Bahria Univ Dept Comp Sci Islamabad 44220 Pakistan Univ Elect Sci & Technol China Inst Fundamental & Frontier Sci Chengdu 610054 Peoples R China King Abdulaziz Univ Fac Comp & Informat Technol Dept Informat Technol Jeddah 21589 Saudi Arabia Univ Elect Sci & Technol China Yangtze Delta Reg Inst Quzhou Quzhou 324000 Peoples R China

Globally, cancer remains a major health challenge due to its high mortality rates. Traditional experimental approaches and therapies are resource-intensive and often cause significant side effects. Anticancer peptides (ACPs) have emerged as alternative therapeutic agents owing to their selectivity, safety, and potential to mitigate drug resistance. In this paper, we propose pACPs-DNN, a novel attention mechanism-based deep learning model developed for the accurate prediction of ACPs and non-ACPs. The pACPs-DNN model transforms input peptides into image representations using residue-wise energy contact matrix (RECM), substitution Matrix Representation (SMR), and Position Specific Scoring Matrix (PSSM) embeddings, followed by local binary pattern (LBP)-based decomposition to capture enhanced structural and local semantic features. These transformations generate novel feature sets, including RECM_LBP, LBP_SMR, and LBP_PSSM. Subsequently, a two-tier feature selection approach is employed to identify a high-ranking optimal feature set, which is then used to train an attention-based deep neural network. The proposed pACPs-DNN model achieves an impressive training accuracy of 96.91 % and an AUC of 0.98. To evaluate its generalization capability, the model was validated on independent datasets, demonstrating significant improvements of 5 % and 3.5 % in accuracy over existing models on the Ind-I and IndII datasets, respectively. The demonstrated efficacy and robustness of pACPs-DNN highlight its potential as a valuable tool for advancing drug discovery and academic research in cancer-related therapeutic development.

关键词： Anticancer peptides Prediction Attention mechanism Deep learning Substitution matrix representation Residue energy contact matrix Evolutionary features local binary pattern

来源：评论

学校读者我要写书评

暂无评论

Effective infrared spectral identification method via improved local binary patterns and spectral description representation

引用

INFRARED PHYSICS & TECHNOLOGY 2023年第1期133卷

作者： An, Qing Li, Tintin Hassan, Mohammed Mehedi Deng, Qian Drouard, Vincent Wuchang Univ Technol Sch Artificial Intelligence 16 Jiang Xia Ave Wuhan 430223 Hubei Peoples R China Univ Calif Los Angeles Dept Mech Engn Los Angeles CA 90095 USA

Infrared spectral identification task which is an essential branch of infrared spectroscopy, has been extensively studied. In this article, we proposed an efficient description representation model with the local binary pattern (LBP) strategy, and applied to infrared spectral searching and identification. The idea is motivated by the fact that spectral curve can be considered as a composition of micro-patterns such as peaks, troughs, valley and flat lines which are well described by LBP. The whole spectral lines are split into several small sections, and then extracted the spectral LBP features. The method is capable of extracting both the local and holistic spectral features and robust to random noise and baseline interferences. Experimental results on the public infrared spectral dataset demonstrate that the proposed spectral identification method can significantly raise performance when noise and baseline interferences are present.

关键词： Infrared spectroscopy Spectral identification local binary pattern Recognition

来源：评论

学校读者我要写书评

暂无评论

Energy-Efficient Sleep Apnea Detection Using a Hyperdimensional Computing Framework Based on Wearable Bracelet Photoplethysmography

引用

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2024年第8期71卷 2483-2494页

作者： Chen, Tian Zhang, Jingtao Xu, Zeju Redmond, Stephen J. Lovell, Nigel H. Liu, Guanzheng Wang, Changhong Sun Yat Sen Univ Sch Biomed Engn Shenzhen Campus Shenzhen 518107 Peoples R China Univ Coll Dublin Sch Elect & Elect Engn Dublin Ireland Univ New South Wales Grad Sch Biomed Engn Sydney NSW Australia

Objective: Sleep apnea syndrome (SAS) is a common sleep disorder, which has been shown to be an important contributor to major neurocognitive and cardiovascular sequelae. Considering current diagnostic strategies are limited with bulky medical devices and high examination expenses, a large number of cases go undiagnosed. To enable large-scale screening for SAS, wearable photoplethysmography (PPG) technologies have been used as an early detection tool. However, existing algorithms are energy-intensive and require large amounts of memory resources, which are believed to be the major drawbacks for further promotion of wearable devices for SAS detection. Methods: In this paper, an energy-efficient method of SAS detection based on hyperdimensional computing (HDC) is proposed. Inspired by the phenomenon of chunking in cognitive psychology as a memory mechanism for improving working memory efficiency, we proposed a one-dimensional block local binary pattern (1D-BlockLBP) encoding scheme combined with HDC to preserve dominant dynamical and temporal characteristics of pulse rate signals from wearable PPG devices. Results: Our method achieved 70.17% accuracy in sleep apnea segment detection, which is comparable with traditional machine learning methods. Additionally, our method achieves up to 67x lower memory footprint, 68x latency reduction, and 93x energy saving on the ARM Cortex-M4 processor. Conclusion: The simplicity of hypervector operations in HDC and the novel 1D-BlockLBP encoding effectively preserve pulse rate signal characteristics with high computational efficiency. Significance: This work provides a scalable solution for long-term home-based monitoring of sleep apnea, enhancing the feasibility of consistent patient care.

关键词： Hyperdimensional computing local binary pattern low power consumption photoplethysmography sleep apnea detection Hyperdimensional computing local binary pattern low power consumption photoplethysmography sleep apnea detection

来源：评论

学校读者我要写书评

暂无评论

Wire rope damage detection based on a uniform-complementary binary pattern with exponentially weighted guide image filtering

引用

VISUAL COMPUTER 2025年第4期41卷 2377-2390页

作者： Liu, Qunpo Tang, Qi Su, Bo Bu, Xuhui Hanajima, Naohiko Wang, Manli Henan Polytech Univ Sch Elect Engn & Automat Jiaozuo Henan Peoples R China Henan Int Joint Lab Direct Dr & Control Intelligen Jiaozuo Henan Peoples R China Muroran Inst Technol Coll Informat & Syst 27-1 Mizumoto Cho Muroran Hokkaido 0508585 Japan

In response to the problem of unclear texture structure in steel wire rope images caused by complex and uncertain lighting conditions, resulting in inconsistent LBP feature values for the same structure, this paper proposes a steel wire surface damage recognition method based on exponential weighted guided filtering and complementary binary equivalent patterns. Leveraging the phenomenon of Mach bands in vision, we introduce a guided filtering method based on local exponential weighting to enhance texture details by applying exponential mapping to evaluate pixel differences within local window regions during image filtering. Additionally, we propose complementary binary equivalent pattern descriptors as neighborhood difference symbol information representation operators to reduce feature dimensionality while enhancing the robustness of binary encoding against interference. Experimental results demonstrate that compared to classical guided filtering algorithms, our image enhancement method achieves improvements in PSNR and SSIM mean values by more than 32.5% and 18.5%, respectively, effectively removing noise while preserving image edge structures. Moreover, our algorithm achieves a classification accuracy of 99.3% on the steel wire dataset, with a processing time of only 0.606 s per image.

关键词： Guided filtering local binary pattern Image enhancement Defect detection

来源：评论

学校读者我要写书评

暂无评论

Regional gradient pattern (RGP): A novel invariant texture descriptor

引用

EXPERT SYSTEMS WITH APPLICATIONS 2025年 276卷

作者： Wang, Xiangyang Xu, Yanqi Niu, Panpan Liaoning Normal Univ Sch Comp Sci & Artificial Intelligence Dalian 116029 Peoples R China

local binary pattern (LBP) and its variants have demonstrated excellent distinguishability in facing different challenges. However, most of these LBP methods use scalar thresholds to encode all neighborhood binarization. There are thus two main problems: 1) they are highly prone to encode two neighborhoods with differences in texture structure as the same LBP code. 2) They cannot describe the interactions between the neighborhood and the information in the region where the neighborhood is located. Given this, this paper proposes a Generalized Multiscale Hierarchical Threshold (GMHT) framework, which can effectively capture texture information at different scales and regions of an image, thus solving the first problem. For the second problem, we propose the Regional Gradient pattern (RGP), which is a 3-D joint and cascade of the gradient operator (G), the extremum operator (E), the variance operator (V) and the center operator (C). Respectively, the four operators encode different texture information of the local and the region to describe the local-region interactions. Benefiting from the calculation method, they are invariant to the grayscale-inversion. Experiments on four texture databases (Outex, KTH-TIPS, CUReT, USPtex1) show that this descriptor achieves state-of-the-art classification results in the presence of linear and even non-linear grayscale-inversion transformations. Our code is available at: https: //***/xuyanqi971202/RGP_***.

关键词： Texture classification local binary pattern Grayscale-inversion Generalized multiscale hierarchical threshold Regional gradient pattern

来源：评论

学校读者我要写书评

暂无评论

An improved method for AI-based smoky vehicle detection from traffic surveillance videos

引用

SIGNAL IMAGE AND VIDEO PROCESSING 2025年第1期19卷 1-8页

作者： Nisha, J. S. Goutham, Veerapu Palanisamy, Gopinath Vellore Inst Technol Sch Elect Engn Vellore India

Huge emissions of smoke from vehicles elevate the risk of respiratory infections, life-threatening diseases like cancer, heart problems, pulmonary diseases, etc. It is an extremely serious task to detect the smoky vehicles for ensuring proper maintenance and limiting hazardous emissions. Smoky vehicle emissions continue to be a critical challenge and an alarming threat in densely populated areas where the quality of air is poor. This paper proposes an automated method for detecting smoky vehicles from traffic surveillance videos using the structural feature extraction technique. To detect the vehicles, the universal background subtraction algorithm Vibe is used, and based on some rules, non-vehicle objects are removed. The features are extracted from the smoky and non-smoky vehicles using a modified structural co-occurrence matrix. Finally, the random forest classifier is used to classify non-smoky and smoky vehicles. The results evince that the proposed method achieves an overall accuracy of 96.50% and outperforms the other state-of-the-art feature extraction methods.

关键词： Structural feature extraction Vibe back ground subtraction local binary pattern GLCM Feature extraction Modified structural co-occurrence matrix

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：