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10th Biennial International Conference on pattern Recognition and Machine Intelligence (PReMI)

作者： Nath, Pranay Mondal, Surajit Ghosh, Lidia Maulana Abul Kalam Azad Univ Technol Kolkata West Bengal India Univ Engn & Management Inst Engn & Management Kolkata West Bengal India

ISBN: (纸本)9783031451690;9783031451706

Advancements in medical science have led to new approaches for preventing, diagnosing, and treating brain tumors, studied by researchers across different fields. The accurate identification of tumors in MRI scans can assist in disease identification, treatment evaluation, and radiation-based therapies. Currently, humans manually perform this task, but research has explored the integration of computer processing in MRI analysis. While MRIs provide a comprehensive view of the brain to identify tumors, they lack accuracy in pinpointing their location and size. To address this, an improved version of local binary pattern (LBP) encoded optimized Convolutional neural network is proposed to improve diagnostic accuracy. LBP captures texture information in a local neighborhood of each pixel, providing additional features to learn from and enhancing the accuracy of texture-based image classification. The model is evaluated on the Figshare dataset through multiple experiments.

关键词： Optimized Convolutional Neural Network local binary pattern Brain tumor Figshare dataset MRI analysis

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Evaluation of Timber Defect Classification Performance using Colour Uniform local binary pattern

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2025年第1期52卷 201-207页

作者： Rahiddin, Rahillda Nadhirah Norizzaty Hashim, Ummi Raba'ah Kanchymalay, Kasturi Salahuddin, Lizawati Ali, Martina Wibawa, Aji Prasetya Malaysia Malaysia Malaysia Malaysia Malaysia Indonesia

This study explores timber defect classification using features extracted from Colour Uniform local binary patterns (CULBP) for four timber species: Rubberwood, KSK, Merbau, and Meranti, evaluating eight common defects such as bark pocket, blue stain, borer holes, brown stain, knot, rot, split, and wane. A series of colour feature sets were extracted from individual and combined RGB channels, with classification performed via an Artificial Neural Network (ANN). Results demonstrate that RGB channels without extensive colour normalisation deliver optimal accuracy, achieving 90.2% - higher than all other channels. Compared to previous studies using grayscale images and other LBP variations, our approach outperformed prior models like Daubechies Wavelet and LBP (85%) and basic LBP methods (65.4%), confirming that integrating RGB colour channels significantly enhances classification. Among timber species, Rubberwood achieved the highest accuracy, with particularly strong results for Rubberwood Rot (92.2%) and Merbau Hole (95.6%), underscoring the advantages of colour-based feature extraction in timber defect classifcation. © (2025), (International Association of Engineers). All rights reserved.

关键词： local binary pattern

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A comprehensively improved local binary pattern framework for texture classification

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Multimedia Tools and Applications 2024年 1-26页

作者： Song, Yuyan Sa, Jiming Luo, Yijie Zhang, Zhushanying School of Information Engineering Wuhan University of Technology Wuhan430070 China Hubei Key Laboratory of Broadband Wireless Communication and Sensor Networks Wuhan University of Technology Wuhan430070 China College of Biomedical Engineering South-Central Minzu University Wuhan430074 China Hubei Key Laboratory of Medical Information Analysis and Tumor Diagnosis & amp Treatment Wuhan430074 China

local binary pattern (LBP) has been widely studied for its remarkable texture feature extraction capabilities. A large number of LBP variants have been proposed. Most of these variants still use the traditional LBPriu2 mapping method to achieve the purpose of dimensionality reduction or improvement of classification accuracy. In fact, LBPriu2 loses much texture information. Inspired by the idea of utilizing complementary information and multi-operator fusion, a mapping method based on triple complementary information (LBPOUS) is proposed in this paper. LBPOUS can further use complementary texture information to provide an alternative mapping solution for LBP variants, and it can be applied to any LBP variants that require mapping. Furthermore, classification accuracy, noise robustness, and computational complexity have always been the major problems for LBP to balance. This paper proposes a comprehensively improved local binary pattern framework (CILBPF). Firstly, the alpha-trimmed mean filter (ATMF) is improved to obtain well-rounded noise robustness. Secondly, LBPOUS is applied to LBP variants to retain richer texture information. Finally, the zero elimination (ZE) is improved to ensure that the feature dimensions are maintained at a relatively low level. Experiments are conducted on five representative public texture datasets, Outex, CUReT, UIUC, UMD, and ALOT. The results show that the proposed CILBPF can effectively improve the classification performance of LBP variants by adopting LBPOUS while ensuring computational complexity. Meanwhile, better handling of Gaussian noise and salt-and-pepper noise is also acquired. It is worth mentioning that the proposed framework also exhibits excellent noise robustness against mixed noise. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： local binary pattern

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A novel sub-windowing local binary pattern approach for dorsal finger creases based biometric classification system

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ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH 2024年 60卷

作者： Riaz, Imran Ali, Ahmad Nazri Ibrahim, Haidi Univ Sains Malaysia Sch Elect & Elect Engn Nibong Tebal 14300 Penang Malaysia Mirpur Univ Sci & Technol Mirpur Inst Technol Mirpur 10250 Azad Kashmir Pakistan

Biometric authentication systems have been widely deployed in various applications, including security systems, bank transactions and authentication on smart electronic devices. Obtaining the salient and distinctive features is very important for achieving high accuracy in biometric authentication systems. local binary pattern (LBP) variants are the best-performing local descriptors and are popular due to computational simplicity and flexibility. However, most of the existing LBP variants consider a 3 x 3 window with one specific central pixel for all neighborhoods, which affects the sensitivity to non-monotonic intensity changes and reduces the robustness of the feature description. Thus, a new variant of LBP called TD-LBP is introduced, which is based on the four Tshape sub-windows and two diagonal (D) regions. Inspired by the sub-windowing approach to capture the microstructure information of the image, TD-LBP first divides the 3 x 3-pixel window into four sub-regions of Tshape structure and then takes two diagonal regions to extract more texture information. Three different classifiers, artificial neural network (ANN), support vector machine (SVM), and k-nearest neighbor (KNN) are employed to evaluate the effectiveness of the proposed approach for dorsal finger crease biometric system. Experiments conducted on the self-collected dorsal finger crease dataset demonstrate the prominent performance and suitability of the proposed TD-LBP for a newly explored finger crease biometric identifier. The proposed approach was able to achieve 96.67 %, 89.26 %, and 82.22 % classification accuracies for ANN, SVM, and KNN classifiers, respectively. Moreover, we clearly validate the viability of the proposed TD-LBP descriptor for the dorsal finger crease biometric trait by comparing the results with state-of-the-art biometric system based LBP descriptors. The significance of the TD-LBP method is demonstrated with improved verification and identification results through receiver

关键词： Dorsal finger crease local binary pattern TD-LBP Feature extraction Classification

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A new local pooling approach for convolutional neural network: local binary pattern

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第12期83卷 34137-34151页

作者： Ozdemir, Cuneyt Dogan, Yahya Kaya, Yilmaz Siirt Univ Comp Engn Dept TR-56100 Siirt Turkiye Batman Univ Comp Engn Dept TR-72000 Batman Turkiye

The pooling layer used in CNN models aims to reduce the resolution of image/feature maps while retaining their distinctive information, reducing computation time and enabling deeper models. Max and average pooling methods are frequently used in CNN models due to their computational efficiency;however, these methods discard the position information of the pixels. In this study, we proposed an LBP-based pooling method that generates a neighborhood-based output for any pixel, reflecting the correlation between pixels in the local area. Our proposed method reduces information loss since it considers the neighborhood and size of the pixels in the pooling region. Experimental studies were performed on four public datasets to assess the effectiveness of the LBP pooling method. In experimental studies, a toy CNN model and various transfer learning models were utilized in conducting test operations. The proposed method provided improvements of 1.56% for Fashion MNIST, 0.22% for MNIST, 3.95% for CIFAR10, and 5% for CIFAR100 dataset using the toy model. In the experimental studies conducted using the transfer learning model, performance improvements of 6.99(-/+)(0.74) and 8.3(-/+)(0.1) were achieved for CIFAR10 and CIFAR100, respectively. We observed that the proposed method outperforms the commonly used pooling layers in CNN models. Code for this paper can be publicly accessed at: https://***/cuneytozdemir/lbppooling

关键词： Pooling methods Convolutional neural network local binary pattern

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Recurrence prediction with local binary pattern-based dosiomics in patients with head and neck squamous cell carcinoma

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PHYSICAL AND ENGINEERING SCIENCES IN MEDICINE 2023年第1期46卷 99-107页

作者： Kamezawa, Hidemi Arimura, Hidetaka Teikyo Univ Fac Fukuoka Med Technol Dept Radiol Technol 6-22 Misaki Machi Omuta Fukuoka 8368505 Japan Kyushu Univ Fac Med Sci Dept Hlth Sci Div Med Quantum Sci 3-1-1 Maidashi Higashi Ku Fukuoka 8128582 Japan

We investigated an approach for predicting recurrence after radiation therapy using local binary pattern (LBP)-based dosiomics in patients with head and neck squamous cell carcinoma (HNSCC). Recurrence/non-recurrence data were collected from 131 patients after intensity-modulated radiation therapy. The cases were divided into training (80%) and test (20%) datasets. A total of 327 dosiomics features, including cold spot volume, first-order features, and texture features, were extracted from the original dose distribution (ODD) and LBP on gross tumor volume, clinical target volume, and planning target volume. The CoxNet algorithm was employed in the training dataset for feature selection and dosiomics signature construction. Based on a dosiomics score (DS)-based Cox proportional hazard model, two recurrence prediction models (DSODD and DSLBP) were constructed using the ODD and LBP dosiomics features. These models were used to evaluate the overall adequacy of the recurrence prediction using the concordance index (CI), and the prediction performance was assessed based on the accuracy and area under the receiver operating characteristic curve (AUC). The CIs for the test dataset were 0.71 and 0.76 for DSODD and DSLBP, respectively. The accuracy and AUC for the test dataset were 0.71 and 0.76 for the DSODD model and 0.79 and 0.81 for the DSLBP model, respectively. LBP-based dosiomics models may be more accurate in predicting recurrence after radiation therapy in patients with HNSCC.

关键词： Recurrence prediction Head and neck carcinoma local binary pattern Dosiomics

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Compressed Hexagonal local binary pattern Using Cyclic Sets for Texture Description

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IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF ELECTRICAL ENGINEERING 2024年第1期49卷 13-34页

作者： Azadimotlagh, Mehdi Fadaei, Sadegh Rashno, Armin Persian Gulf Univ Jam Fac Engn Dept Comp Engn Bushehr *** Iran Univ Yasuj Fac Engn Dept Elect Engn Yasuj *** Iran Lorestan Univ Fac Engn Dept Comp Engn Lorestan *** Iran

This paper presents a new coding pattern based on conventional LBP in hexagonal lattice using cyclic sets referred as compressed hexagonal local binary pattern (CHLBP). For this task, first, conditions of having a consecutive sequence of 1's in patterns are investigated. Then, conventional LBP patterns are divided into three sets and then each set is mapped into a new set using cyclic set theorem. Finally, feature vector is defined based on new patterns. CHLBP has been evaluated on four datasets and compared with several state-of-the-art methods with respect to two criteria precision and recall. The best performance of CHLBP is achieved in ORL dataset with the precision of 89.51%. Also, CHLBP is by far the best method in KDEF dataset which improves the precision of the best previous method by 7%. Finally, from runtime and feature vector length point of view, CHLBP is the fastest method in all datasets.

关键词： local binary pattern Hexagonal lattice Square to hexagonal image Cyclic sets

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Early detection of Alzheimer's disease using squeeze and excitation network with local binary pattern descriptor

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pattern ANALYSIS AND APPLICATIONS 2024年第2期27卷 54-54页

作者： Francis, Ambily Pandian, S. Immanuel Alex Sagayam, K. Martin Dang, Lam Anitha, J. Dinh, Linh Pomplun, Marc Dang, Hien Karunya Inst Technol & Sci Dept Elect & Commun Coimbatore Tamil Nadu India Sahrdaya Coll Engn & Technol Dept Elect & Commun Kodakara Kerala India INSA Lyon Dept Comp Sci Villeurbanne France Karunya Inst Technol & Sci Dept Comp Sci & Engn Coimbatore Tamil Nadu India Suffolk Univ Dept Informat Syst Boston MA USA Univ Massachusetts Dept Comp Sci Boston MA USA Molloy Univ Dept Math & Comp Sci Rockville Ctr NY 11570 USA Thuyloi Univ Fac Comp Sci & Engn Hanoi Vietnam

Alzheimer's disease is a degenerative brain disease that impairs memory, thinking skills, and the ability to perform even the most basic tasks. The primary challenge in this domain is accurate early stage disease detection. When the disease is detected at an early stage, medical professionals can prescribe medications to reduce brain shrinkage. Although the disease may not be curable, these interventions can extend the patient's life by slowing down the rate of shrinkage. The four cognitive states of the human brain are cognitive normal (CN), mild cognitive impairment convertible (MCIc), mild cognitive impairment non-convertible (MCInc), and Alzheimer's disease (AD). Mild cognitive impairment convertible (MCIc) is the early stage of Alzheimer's disease. Individuals with MCIc will develop Alzheimer's disease for a few years. However, it is difficult to detect this state through medical investigations. The mild cognitive impairment non-convertible state (MCInc) is the state immediately before MCIc. MCInc is a common condition in people of all ages, where minor memory issues arise as a result of normal aging. Early detection of AD can be claimed if and only if the transition from MCInc to MCIc is complete. Deep learning algorithms can be promising techniques for identifying the progression stage of a disease using magnetic resonance imaging. In this study, a novel deep learning algorithm was proposed to improve the classification accuracy of MCIc vs. MCInc. This study utilized the advantages of local binary patterns along with squeeze and excitation networks (SENet). Without the squeeze and excitation network, the classification accuracy of MCIc versus MCInc was 82%. The classification accuracy improved by 86% with the use of SENet. The experimental results show that the proposed model achieves better performance for MCInc vs. MCIc classification in terms of accuracy, precision, recall, F1 score, and ROC.

关键词： Alzheimer's disease Mild cognitive impairment convertible Mild cognitive impairment non-convertible local binary pattern Squeeze and excitation networks Convolutional neural network

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Texture classification combining improved local binary pattern and threshold segmentation

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第17期82卷 25899-25916页

作者： Luo, Yijie Sa, Jiming Song, Yuyan Jiang, He Zhang, Chi Zhang, Zhushanying Wuhan Univ Technol Sch Informat Engn Wuhan 430070 Peoples R China Wuhan Univ Technol Hubei Key Lab Broadband Wireless Commun & Sensor N Wuhan 430070 Peoples R China Hubei Key Lab Med Informat Anal & Tumor Diag & Tre Wuhan 430074 Peoples R China South Cent Minzu Univ Coll Biomed Engn Wuhan 430074 Peoples R China South Cent Minzu Univ Key Lab Cognit Sci State Ethn Affairs Commiss Wuhan 430074 Peoples R China

local binary pattern (LBP) is a feature extraction operator with both high texture discrimination ability and low computational complexity. Many LBP variants have been proposed to improve the performance of texture classification or overcome the drawbacks of LBP. There are three shortcomings in some LBP variants: discarding the magnitude component between local differences, adopting fixed weights in the encoding process and discarding the absolute information of the pixel gray level. Based on the three points, this paper proposes an improved LBP with two operators, local binary pattern operator based on magnitude ranking and global threshold segmentation operator, to further improve the performance. This improved LBP can achieve excellent texture classification accuracy across six common datasets, with an average of 1% lower than the best LBP variants. Meanwhile, the computational complexity of the proposed improved LBP is several times lower than that of the best LBP variants.

关键词： Texture classification local binary pattern Feature extraction

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Image splicing detection with principal component analysis generated low-dimensional homogeneous feature set based on local binary pattern and support vector machine

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第17期82卷 25847-25864页

作者： Das, Debjit Naskar, Ruchira Chakraborty, Rajat Subhra Indian Inst Engn Sci & Technol Dept Informat Technol Sibpur 711103 India Indian Inst Technol Dept Comp Sci & Engn Kharagpur 721302 India

Due to the wide availability of various ready-to-use image tampering software tools, image manipulation has become widespread nowadays. Tampered images are often used intentionally for unlawful and malicious purposes. One of the most common forms of image manipulation today includes the Image Splicing attack. Image splicing can be defined as generating a composite image by combining parts of different images from different sources, forming one artificially generated composite image. Given its prevalence, image splicing detection has a fundamental importance in digital forensics, and therefore, several research works have been carried out in the recent past to address this problem. In this paper, we propose an image forensic technique using a homogeneous image feature set of optimal dimensions for the successful detection of image splicing attacks. The Columbia Image Splicing Detection Evaluation Dataset has been used in our experiments, and different feature sets extracted from the available data have been adopted and experimented with. We have succeeded in finding a feature set of dimension as low as 31 comprising of local binary pattern features for image splicing detection, obtaining state-of-the-art spliced image classification result using the Support Vector Machine classifier.

关键词： Classification Digital forensics Feature dimension Image splicing local binary pattern Principal component analysis

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