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local Tetra-Directional pattern-A New Texture Descriptor for Content-Based Image Retrieval

pattern RECOGNITION AND IMAGE ANALYSIS 2020年第4期30卷 578-592页

作者： Bedi, Anterpreet Kaur Sunkaria, Ramesh Kumar Dr BR Ambedkar Natl Inst Technol Dept Elect & Commun Engn Jalandhar 144011 Punjab India

In this present work, a new technique for content-based image retrieval is introduced using local tetra-directional pattern. In conventional local binary pattern (LBP), each pixel of an image is changed into a specific binary pattern in accordance with their relationship with neighbouring pixels. Texture feature descriptor introduced in this work differs from local binary pattern as it exploits local intensity of pixels in four directions in the neighbourhood. Also, colour feature and gray level co-occurrence matrix have been applied in this work. Median of images have also been taken under consideration to keep the edge information preserved. The proposed technique has been validated experimentally by conducting experiments on two different sets of data, viz., Corel-1K and AT&T. Performance was measured using two well-known parameters, precision and recall, and further comparison was carried with some state-of-the-art local patterns. Comparison of results show substantial improvement in the proposed technique over existing methods.

关键词： content based image retrieval local tetra-directional pattern feature vector gray level co-occurrence matrix local binary pattern

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local binary patterning Approach for Movement Related Potentials based Brain Computer Interface 8

Local Binary Patterning Approach for Movement Related Potent...

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8th IEEE International Workshop on Advances in Sensors and Interfaces (IWASI)

作者： Mezzina, Giovanni De Venuto, Daniela Politecn Bari Dept Elect & Informat Engn Via E Orabona 4 I-70125 Bari Italy

ISBN: (纸本)9781728105567

This paper proposes the design and the implementation of an innovative algorithm for a 2-choices synchronous Brain-Computer Interface (BCI). The proposed BCI operates on signals from eight EEG channels evenly distributed along the sensorimotor area. The acquired EEGs are then analyzed by using a symbolization method. Typically, the symbolization includes data-analysis algorithms that translate physical processes from experimental measurements into a series of discrete symbols (e.g., bit strings). For the BCI application, the chosen symbolization algorithm is the local binary pattern (LBP). Since the selected LBP method uses binary operations for the whole processing chain (end-to-end), the complexity and the computing timing of the features extraction (FE) and real-time classification stages have been strongly reduced. Finally, a time-continuous Support Vector Machine (tcSVM) classifies the LBP-extracted features. The here proposed BCI algorithm has been validated on 3 subjects (aged 26 +/- 1), who underwent a stimulation protocol oriented to Movement-Related Potentials (MRPs) elicitation. The in-vivo validation showed how the system is able to reach an intention recognition accuracy of 85.61 +/- 1.19 %. In addition, starting from the complete data storage, the whole implemented computing chain asks, on average, for just similar to 3ms to provide the classification. As a proof of concept, the tcSVM outcomes have been used to drive, via Bluetooth, a 3D printed robotic hand.

关键词： Brain Computer Interface EEG local binary pattern Symbolization Robotic Hand

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Salient local binary pattern for Ground-Based Cloud Classification

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Acta meteorologica Sinica 2013年第2期27卷 211-220页

作者：刘爽王春恒肖柏华张重邵允学 State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences

Ground-based cloud classification is challenging due to extreme variations in the appearance of clouds under different atmospheric conditions. Texture classification techniques have recently been introduced to deal with this issue. A novel texture descriptor, the salient local binary pattern （SLBP）, is proposed for ground-based cloud classification. The SLBP takes advantage of the most frequently occurring patterns （the salient patterns） to capture descriptive information. This feature makes the SLBP robust to noise. Experimental results using ground-based cloud images demonstrate that the proposed method can achieve better results than current state-of-the-art methods.

关键词： salient local binary pattern local binary pattern ground-based cloud classification

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Human Identification using local binary patterns for Finger Outer Knuckle 8

Human Identification using Local Binary Patterns for Finger ...

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8th IEEE Conference on Systems, Process and Control, ICSPC 2020

作者： Al-Nima, Raid Rafi Omar Jarjes, Muhammed Khidr Kasim, Ahmed Waled Sheet, Sinan S. Mohammed Technical Engineering College Northern Technical University Iraq

ISBN: (纸本)9781728188614

Finger Outer Knuckle (FOK) has been considered as one of the attractive biometric characteristics. A new method is proposed and called local binary patterns for FOK (LBP-FOK). It consists of two types of vectors horizontal and vertical. This study concentrates on the human identification which is based on FOK dataset of type Indian Institute of Technology Delhi Finger Knuckle (IITDFK). The major feature of just middle finger has been considered. The best identification accuracy is 85.97% has been achieved. The calculation of average comparison of the two vectors has been deployed. © 2020 IEEE.

关键词： local binary pattern

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Thepade's sorted block truncation coding applied on local binary patterns of images for splicing identification using machine learning classifiers

Thepade's sorted block truncation coding applied on local bi...

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2020 IEEE Bombay Section Signature Conference, IBSSC 2020

作者： Thepade, Sudeep D. Bakshani, Divesh M. Bhingurde, Tanvi Burghate, Shivaji Deshmankar, Shreepad Pimpri Chinchwad College of Engineering Department of Computer Engineering Pune India P.E.S.'s Modern College of Engineering Department of Computer Engineering Pune India

ISBN: (纸本)9781728189932

The era of digitization has accelerated communication and information sharing immensely. With ever-growing digital advancements in technology and applications cybersecurity poses to be a pressing issue. The amount of growth in data exchange is exponential thus making automated processes a vital tool to deliver security. Image editing technologies manipulate image data and have enabled all types of users to tamper images resulting in widespread fake images. Distorted information carries heavy consequences and thus a reliable image forgery detection system is essential. This paper proposes a machine learning-based approach for image splicing detection using the global and local characteristics of the image. TSBTC N-ary, with the value of N = 12, 14 and 16, is applied along with LBP for feature extraction and various Machine learning classifiers are implemented and compared for image splicing detection. The performance of the proposed method is tested and validated on 3 benchmark datasets: CASIA V1 Dataset, Columbia Dataset, and Columbia Uncompressed Dataset. Results are evaluated based on various performance metrics. © 2020 IEEE.

关键词： local binary pattern

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Histopathological Image Analysis Using Machine Learning to Evaluate Cisplatin and Exosome Effects on Ovarian Tissue in Cancer Patients

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APPLIED SCIENCES-BASEL 2025年第4期15卷 1984-1984页

作者： Senturk, Tugba Latifoglu, Fatma Altintop, Cigdem Guluzar Yay, Arzu Gonen, Zeynep Burcin Onder, Gozde Ozge Mat, Ozge Cengiz Ozkul, Yusuf Inonu Univ Dept Biomed Engn TR-44280 Malatya Turkiye Erciyes Univ Dept Biomed Engn TR-38039 Kayseri Turkiye Erciyes Univ Fac Med Dept Histol & Embryol TR-38039 Kayseri Turkiye Erciyes Univ Genome & Stem Cell Ctr Dept Oral & Maxillofacial Surg TR-38039 Kayseri Turkiye Erciyes Univ Fac Med Dept Med Genet TR-38039 Kayseri Turkiye

Cisplatin, a widely used chemotherapeutic agent, is highly effective in treating various cancers, including ovarian and lung cancers, but it often causes ovarian tissue damage and impairs reproductive health. Exosomes derived from mesenchymal stem cells are believed to possess reparative effects on such damage, as suggested by previous studies. This study aims to evaluate the reparative effects of cisplatin and exosome treatments on ovarian tissue damage through the analysis of histopathological images and machine learning (ML)-based classification techniques. Five experimental groups were examined: Control, cisplatin-treated (Cis), exosome-treated (Exo), exosome-before-cisplatin (ExoCis), and cisplatin-before-exosome (CisExo). A set of 177 local binary pattern (LBP) features were extracted from histopathological images, followed by feature selection using Lasso regression. Classification was performed using ML algorithms, including decision tree (DT), k-nearest neighbors (KNN), support vector machine (SVM), and Artificial Neural Network (ANN). The CisExo group exhibited the most homogeneous texture, suggesting effective tissue recovery, whereas the ExoCis group demonstrated greater heterogeneity, possibly indicating incomplete recovery. KNN and ANN classifiers achieved the highest accuracy, particularly in comparisons between the Control and CisExo groups, reaching an accuracy of 87%. The highest classification accuracy was observed for the Control vs. Cis groups (approximately 91%), reflecting distinct features, whereas the Control vs. Exo groups demonstrated lower accuracy (around 68%) due to feature similarity. Exosome treatments, particularly when administered post-cisplatin, significantly improve ovarian tissue recovery. This study highlights the potential of ML-based classification as a robust tool for evaluating therapeutic outcomes. Additionally, it underscores the promise of exosome therapy in mitigating chemotherapy-induced ovarian damage and preserving r

关键词： cisplatin exosome local binary pattern k-nearest neighbors support vector machine artificial neural network decision tree

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Color Context binary pattern Using Progressive Bit Correction for Image Classification

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Chinese Journal of Electronics 2021年第3期30卷 471-481页

作者： SONG Tiecheng FENG Jie LI Shuang ZHANG Tianqi School of Communication and Information Engineering Chongqing University of Posts and Telecommunications

local binary pattern(LBP) is sensitive to noise. Noise-resistant LBP(NRLBP) addresses this problem by thresholding local neighboring pixels into three-valued states(i.e., 0, 1 and uncertain bits)and recovering uncertain bits via an error-correction mechanism. In this paper, we extend NRLBP to deal with color images and propose a robust color image descriptor called Color context binary pattern(CCBP). In CCBP, we employ scale context and neighbor context to progressively correct the encoded bits. First, we encode intra-channel local neighboring pixels into three-valued states in scale space and use majority voting to correct all states across scales. Then, we compute inter-channel color feature distances and correct the uncertain bits via neighboring bit propagation. Finally, we construct the image descriptor by concatenating all histograms based on the corrected binary codes. Experiments on four benchmark databases demonstrate the robustness of CCBP for color image classification under very low signal-to-noise ratio levels.

关键词： scale context intra-channel local neighboring pixels image classification color images corrected binary codes color image classification feature extraction local binary pattern bit propagation NRLBP three-valued states CCBP image coding binary codes progressive bit correction error correction codes uncertain bits Color context binary pattern neighbor context inter-channel color feature distances robust color image descriptor image texture error-correction mechanism noise-resistant LBP image colour analysis encoded bits

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A high accuracy method for the sintering condition recognition of rotary kiln

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MEASUREMENT 2024年 229卷

作者： Hu, Wenyu Mao, Zhizhong Wuhan Univ Inst Next Generat Power Syst & Int Stand Wuhan 430072 Hubei Peoples R China Wuhan Univ Coll Elect Engn & Automat Wuhan 430072 Hubei Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Liaoning Peoples R China

According to the different material sintering conditions, the sintering conditions of alumina rotary kiln be divided into: super -heated, super -chilled, and normal. In this paper, based on local binary pattern and the primary -color method, a novel feature extraction method is proposed to obtain information temperature in flame images without calibrating camera parameters. Through the analysis of the problem well as the experimental phenomena, a new classification procedure is devised: in the first step, the super chilled condition is first separated, in the second step, the normal and super -heat condition are classified. Different feature extraction methods are used in the two steps mentioned above. One is to extract the texture features of pseudo temperature images, and short -time energy is used to describe the dynamic features. other is to extract the texture features of grey -images by our improved LBP ��2 P ,�� , and characterize the dynamics with sample entropy and variance. Finally, experimental results show that the our feature extraction method can effectively reduce intra-class variation, increase inter -class variation and receive a high classification accuracy.

关键词： Sintering condition recognition Rotary kiln Flame image local binary pattern

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Robust zero-watermarking algorithm via multi-scale feature analysis for medical images

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JOURNAL OF INFORMATION SECURITY AND APPLICATIONS 2025年 89卷

作者： Wang, Xiaochao Du, Qianqian Du, Ling Zhang, Huayan Hu, Jianping Tiangong Univ Sch Math Sci Tianjin 300387 Peoples R China Tiangong Univ Dept Software Tianjin 300387 Peoples R China Tiangong Univ Sch Comp Sci & Technol Tianjin 300387 Peoples R China Northeast Elect Power Univ Sch Sci Jilin 132012 Peoples R China

With the rapid growth of information technology, the development and implementation of copyright protection for medical images has become crucial. In this paper, we develop a distinguishable zero-watermarking algorithm via multi-scale feature analysis for medical images. We first detect the global features of the image with speeded-up robust features (SURF) and select the feature regions from the image through texture analysis. Then, we adopt local binary pattern (LBP) to detect the local texture features of these feature areas, and perform singular value decomposition (SVD) to extract the scale features and the detail features;these features are fused to form the feature matrix, and the average hash (aHash) algorithm is applied to the feature matrix to generate the binary feature map. Finally, we perform exclusive-or (XOR) operation between the feature images and the watermark image to generate zero-watermarks, which will be stored in the copyright protection center for further copyright authentication. Experimental results show that the average NC value of the proposed algorithm reaches 0.99 under most attacks, and the average BER of similar image extraction watermark keep below 0.27, which outperforms the current state-of-the-art (SOTA) watermarking algorithms.

关键词： Zero-watermarking Medical images Texture features local binary pattern Singular value decomposition

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Enhancing local binary patterns for higher accuracy in Fatty Liver classification using Deep Learning 5

Enhancing Local Binary Patterns for higher accuracy in Fatty...

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5th IEEE International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2020

作者： Javed, Muhammad Arslan Alsadoon, Abeer Prasad, P.W.C. Rashid, Tarik A. Maag, Angelika Murugesan, Yahini Prabha Study Group Australia Information Technology Sydney Campus Australia University of Kurdistan Hewler Computer Science Erbil KRG Iraq

ISBN: (纸本)9781728194370

In deep learning, local binary patterns (LBP) are inefficient for the textural feature-based classification of the fatty liver because they lose some of the relevant features. The purpose of this study is to enhance classification accuracy. We analyze accuracy and processing time. The proposed system con-sists of a convolutional neural network with curvelet local binary pattern for feature extraction which improves accuracy and can also now determine the size of the fatty liver. Accuracy is measured using probability scores and processing time is measured with total execution time, using sample image groups from CT/MRI images. Results shows that the proposed solution has improved the classification accuracy to 98% from 94% on average and reduced the processing time to 0.313 seconds compared to the existing 0.561 seconds. Moreover, the proposed system has added a volume feature, a, green border represents the volume of the fatty liver. Overall, the proposed system has improving accuracy and processing time required for fatty liver detection whilst leaving desirable features of the best current solution intact. © 2020 IEEE.

关键词： local binary pattern

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