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Mean distance local binary pattern: a novel technique for color and texture image retrieval for liver ultrasound images

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MULTIMEDIA TOOLS AND APPLICATIONS 2021年第14期80卷 20773-20802页

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

A rapid growth in medical ultrasound database makes it difficult for medical practitioners to manage and search relevant data with good efficiency. Hence, a novel image retrieval technique using Mean Distance local binary pattern (Mean Distance LBP) has been proposed for content-based image retrieval. The conventional local binary pattern (LBP) converts every pixel of image into a binary pattern based on their relationship with neighbourhood pixels. The proposed feature descriptor differs from local binary pattern as it transforms the mutual relationship of all neighbouring pixels in a binary pattern based on their standard deviation templates as well as Euclidean distance from the center pixel. Color feature and Gray Level Co-occurrence Matrix have also been used in this work. To prove the excellence of the proposed method, experiments have been conducted on two different databases of natural images and face images. Further, the method is applied on real time ultrasound database for retrieval of liver images from a set of ultrasound images of various organs. The performance has been observed using well-known evaluation measures, precision and recall, and compared with some state-of-art local patterns. Comparison shows a significant improvement in the proposed method over existing methods.

关键词： local binary pattern Mean distance local binary pattern Content based image retrieval Gray level co-occurrence matrix Ultrasound database

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A novel approach for facial expression recognition using local binary pattern with adaptive window

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MULTIMEDIA TOOLS AND APPLICATIONS 2021年第2期80卷 2243-2262页

作者： Kola, Durga Ganga Rao Samayamantula, Srinivas Kumar JNTUK Dept ECE UCEK Kakinada 533003 India

Facial Expression Recognition (FER) is an important area in human computer interaction. FER has different applications such as analysis of student behaviour in virtual class room, driver mood detection, security systems, and medicine. The analysis of facial expressions is an interesting and exciting problem. Feature extraction plays important role in any FER system. local binary pattern (LBP) and its variants are popular for feature extraction due to simplicity in computation and monotonic illumination invariant property. However, the performance of LBP is poor in the presence of noise. This work proposes a novel approach for feature extraction to improve the performance of the FER. In this approach, the LBP is calculated considering 4-neighbors and diagonal neighbours separately. Further, for affective feature description, the concept of adaptive window and averaging in radial directions is introduced. This approach reduces the length of the feature vector as well as immune to noise. Support Vector Machine (SVM) is considered for classification. Recognition rate and confusion matrix are used to assess the performance of the proposed algorithm. Extensive experimental results on JAFFE, CK, FERG and FEI face databases show significant improvement in recognition rate compared to the available techniques both in noise free and noisy conditions.

关键词： Adaptive window Facial expression recognition Feature extraction local binary pattern Support vector machine

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A Median Filtering Forensics CNN Approach Based on local binary pattern 11th

A Median Filtering Forensics CNN Approach Based on Local Bin...

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11th International Conference on Computer Engineering and Networks, CENet2021

作者： Zhu, Tao Gu, Haiyan Chen, Zenan Department of Computer Information and Cyber Security Jiangsu Police Institute Nanjing210031 China Jiangsu Province Electronic Data Forensics and Analysis Engineering Research Center Nanjing210031 China Key Laboratory of Digital Forensics of Jiangsu Provincial Public Security Department Nanjing210031 China

ISBN: (纸本)9789811665530

Median filtering forensics has gradually become a research hotspot because of the wide application of the traditional median filtering (MF) in image tampering and anti-forensics. The difficulty of traditional median filtering based on machine learning forensics is feature extraction which is a manual selection process, and the Convolutional Neural Network (CNN) cannot also well perceive the traces left by median filtering straightly. By taking the local binary pattern (LBP) data of an image as a presentation of the streaking artifact that is a very strong indication for median filtering, a median filtering forensics CNN approach with LBP is proposed, which can automatically learn and get median filtering features from image. Different from traditional CNN, an LBP perception layer is added before the following deep learning layers. Then, five convolution layers constitute the feature extraction group. Finally, the classifier group is composed of three full connected layers to decide whether the image is median filtered or not. The proposed approach is tested on several experiments and the experimental results demonstrate its effectiveness. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： local binary pattern

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Early detection of Alzheimer's disease using local binary pattern and convolutional neural network

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MULTIMEDIA TOOLS AND APPLICATIONS 2021年第19期80卷 29585-29600页

作者： Francis, Ambily Pandian, Immanuel Alex Karunya Inst Technol & Sci Dept Elect & Commun Coimbatore Tamil Nadu India Sahrdaya Coll Engn & Technol Dept Elect & Commun Kodakara Kerala India

Alzheimer's disease, a progressive and irreversible abnormality of the human brain impairs memory and thinking skills. Gradually, it will damage the ability to carry out simple tasks. Even though the disease cannot be completely cured by medical specialists, the rate of brain damage can be pared if the disease is identified in its budding stage itself. Thus, victims and their relatives will get ample time to prepare themselves. Alzheimer's disease (AD), cognitively normal (CN), mild cognitive impairment convertible (MCIc), and mild cognitive impairment non-convertible (MCInc) are the different phases of cognition. The state of memory loss in aged people, which will not lead to AD, can be encountered as MCInc. The state-MCIc gradually leads to AD. The work is intended for the early detection of AD. Early detection can be claimed if and only if the state-MCIc is detected. But the clinical visual identification of state-MCIc from MRI scan is difficult. In this work, a novel local feature descriptor is proposed for the detection of state-MCIc. The proposed local feature descriptor combined strengths of fast Hessian detector and local binary pattern texture operator for the identification of key points and descriptions. A simple convolutional neural network is used for classification. The classification accuracy between MCIc and CN is obtained as 88.46% which is a pivotal result for early detection of AD. The classification accuracy between AD and CN is attained at 88.99%. The results indicate that the proposed system can contribute a colossal innovation in the early detection of AD.

关键词： Alzheimer's disease Mild cognitive impairment convertible Mild cognitive impairment non-convertible Fast Hessian matrix local binary pattern Convolutional neural network

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Wavelet local binary pattern (WLBP): A Novel local Descriptor in Harsh Light Variations 4

Wavelet Local Binary Pattern (WLBP): A Novel Local Descripto...

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4th International Conference on Inventive Research in Computing Applications, ICIRCA 2022

作者： Karanwal, Shekhar Cse Department UK Dehradun India

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

LBP and most of its variants performed well in moderate light changes. But under harsh light changes their performances are not effective. So there is a need of more productive descriptor in harsh light variations. With this note, the proposed work introduces a novel descriptor under harsh light variations so-called Wavelet LBP (WLBP). Initially input image is passed through wavelet decomposition (by using haar wavelet at level 1), which generates four sub-bands. First is approx element and other three are detail elements. Further LBP is deployed to all four sub-bands for feature extraction. It has been noticed during the experiments that histogram of the transformed image doesn't perform impressive in harsh light variations. In contrary, map feature beats the histograms feature comprehensively. Therefore the map LBP features from all four sub-bands are integrated into one framework called as WLBP. FLDA is utilized for the feature compaction and matching is achieved from SVMs. Results are done on YB and EYB datasets, whose images are severely deteriorated by light variations. The accuracy achieved by WLBP is well par from previous histogram based descriptors. WLBP also outperforms all literature methods. © 2022 IEEE.

关键词： local binary pattern

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An adaptive weight texture classification method based on local binary pattern Variance 22

An adaptive weight texture classification method based on Lo...

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3rd International Symposium on Artificial Intelligence for Medical Sciences, ISAIMS 2022

作者： Chen, Hao Tang, Wending Tao, Ran Department of Electrical and Computer Engineering Northeastern University United States Department of Engineering and Physics Tsinghua University China State Key Laboratory of Surface Physics Department of Physics Advanced Materials Laboratory Fudan University China

ISBN: (纸本)9781450398442

The extraction of local texture information using the traditional local binary Mode (LBP) is limited, and it ignores the representation of global texture information, which leads to an unsatisfactory outcome for the texture classification task. local binary Mode (LBP) has been widely used in texture classification. This paper utilizes LBPV to resolve this issue (local binary pattern Variance) and proposes a novel adaptive weight joint multi-scale LBPV2 texture picture classification algorithm. The typical variance weight is replaced by the square of covariance as the cumulative weight of the histogram in this method, and the multi-scale texture information is retrieved using an adaptive weight and multi-scale scheme. Thus, the texture classification performance is further improved. Simulation experiments on the commonly used Outex reference texture database show that the proposed adaptive weight combined with multi-scale LBPV2 can significantly improve the performance of texture classification. In the fields of computer vision and pattern recognition, texture analysis is a fundamental visual problem with a wide range of applications, including object detection, remote sensing, content-based image retrieval, and medical picture analysis. For various research questions, numerous academics have put forth various LBP versions in recent years. The dominant LBP [2] model was proposed by Liao et al. in 2009, and it was empirically chosen as the best model out of all the models. Guo et al. proposed LBPV [3], which expresses local contrast information into the straight square of texture images using a local variance confidence and global matching scheme. In order to increase classification performance, the author also proposed a Completed local binary pattern (CLBP) [4] in the same year. This pattern combines three complimentary groups - CLBPS, CLBPM, and CLBPC - using a combined probability distribution. To enhance the traditional local binary pattern's noise resistance and

关键词： local binary pattern

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MB-ZZLBP: Multiscale Block ZigZag local binary pattern for Face Recognition 2nd

MB-ZZLBP: Multiscale Block ZigZag Local Binary Pattern for F...

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2nd International Conference on Machine Learning, Advances in Computing,Renewable Energy and communication, MARC 2020

作者： Karanwal, Shekhar Diwakar, Manoj Dehradun Uttarakhand India

ISBN: (纸本)9789811623530

This work presents the LBP variant so-called MB-ZZLBP under illumination and expression variations. In MB-ZZLBP, initially mean is computed for all the square sub-blocks (size 2 × 2) of the 6 × 6 pixel window. After the mean computation, 3 × 3 window (pixel) is produced. Then ZigZag pixels are compared with each other, or in other words, the higher-order pixels are subtracted from the lower-order pixels. Differences of those which produce value ≥0 are allocated the label 1, else 0. After encoding each pixel position (binary pattern) eventually results in MB-ZZLBP transformed image. The MB-ZZLBP image is further divided into 3 × 3 sub-regions for extraction of histograms. The fused histogram is the feature size of MB-ZZLBP. The proposed FR approach attains remarkable results on EYB and Faces94 databases. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： local binary pattern

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Face Recognition System Using local binary pattern with binary Dragonfly Algorithm to Feature Selection

Face Recognition System Using Local Binary Pattern with Bina...

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2022 International Conference on Artificial Intelligence of Things, ICAIoT 2022

作者： Al-Aloosi, Ahmed Raad Farhan, Hameed Mutlag Naseri, Raghda Awad Shaban Turkben, Ayca Kurnaz Mustafa, Ahmed Khalid Al-Obadi, Mohammed G.F. Altinbas University Department of Information Technology Istanbul Turkey Altinbas University Department of Electrical and Computer Engineering Istanbul Turkey Altinbas University Department of Software Engineering Istanbul Turkey

ISBN: (纸本)9798350396768

In this paper, the local binary pattern is used to feature extraction, and the binary Dragonfly Algorithm (BDA) is exploited to find the optimum features. This is a new methodology for face recognition systems. A proposed face acknowledgment framework was created to be utilized for various purposes. We utilized a local binary pattern for extraction of the important feature of the face that prepares the information and afterward, we utilized BDA to find the best features from feature data. We will execute and assess the proposed strategy on ORL and other datasets with MATLAB 2021a. © 2022 IEEE.

关键词： local binary pattern

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Multilevel weather detection based on images: a machine learning approach with histogram of oriented gradient and local binary pattern-based features

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JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS 2021年第5期25卷 513-532页

作者： Khan, Md Nasim Das, Anik Ahmed, Mohamed M. Wulff, Shaun S. Univ Wyoming Dept Civil & Architectural Engn 1000 E Univ AveDept 3295 Laramie WY 82071 USA Univ Wyoming Dept Math & Stat Laramie WY 82071 USA

The primary objective of this study was to develop a trajectory-level weather detection system capable of providing real-time weather information at the road surface level using only a single video camera. Two texture-based features, including histogram of oriented gradient (HOG) and local binary pattern (LBP), were extracted from images and used as classification parameters to train the weather detection models using several machine learning classifiers, such as gradient boosting (GB), random forest (RF), and support vector machine (SVM). In addition, a unique multilevel model, based on a hierarchical structure, was also proposed to increase detection accuracy. Evaluation results revealed that the multilevel model provided an overall accuracy of 89.2%, which is 3.2%, 7.5%, and 7.9% higher compared to the SVM, RF, and GB model, respectively, using the HOG features. Considering the LBP features, the multilevel model also produced the best performance with an overall accuracy of 91%, which is 1.6%, 8.6%, and 9% higher compared to the SVM, RF, and GB models, respectively. A sensitivity analysis using the proposed multilevel model revealed that the classification accuracy improved with the increasing number of HOG and LBP features at the expense of more computational powers. The proposed weather detection method is cost-efficient and can be made widely available mainly due to the recent booming of smartphone cameras and can be used to expand and update the current weather-based variable speed limit (VSL) systems in a connected vehicle (CV) environment.

关键词： Histogram of oriented gradient local binary pattern machine learning variable speed limit weather detection

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local binary pattern-based on-road vehicle detection in urban traffic scene

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pattern ANALYSIS AND APPLICATIONS 2020年第4期23卷 1505-1521页

作者： Hassaballah, M. Kenk, Mourad A. El-Henawy, Ibrahim M. South Valley Univ Comp Sci Dept Fac Comp & Informat Qena 83523 Egypt South Valley Univ Fac Sci Math Dept Qena 83523 Egypt Zagazig Univ Comp Sci Dept Fac Comp & Informat Zagazig Egypt

For intelligent traffic monitoring systems and related applications, detecting vehicles on roads is a vital step. However, robust and efficient vehicles detection is still a challenging problem due to variations in the appearance of the vehicles and complicated background of the roads. In this paper, we propose a simple and effective vehicle detection method based on local vehicle's texture and appearance histograms feed into clustering forests. The interdependency of vehicle's parts locations is incorporating within a clustering forests framework. local binary pattern-like descriptors are utilized for texture feature extraction. Through utilizing the LBP descriptors, the local structures of vehicles, such as edge, contour and flat region can be effectively depicted. The align set of histograms generated concurrence with LBPs spatial for random sampled local regions are used to measure the dissimilarity between regions of all training images. Evaluating the fit between histograms is built in clustering forests. That is, clustering discriminative codebooks of latent features are used to search between different LBP features of the random regions utilizing the Chi-square dissimilarity measure. Besides, saliency maps built by the learnt latent features are adopted to determine the vehicles locations in test image. Effectiveness of the proposed method is evaluated on different car datasets stressing various imaging conditions and the obtained results show that the method achieves significant improvements compared to published methods.

关键词： Intelligent traffic monitoring Vehicle detection local binary pattern Saliency map Clustering forests

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