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local binary pattern Based Robust Coverless Information Hiding Approach

SN Computer Science 2025年第1期6卷 1-15页

作者： Gautam, Sangeeta Kumar, Manoj Department of Computer Science Babasaheb Bhimrao Ambedkar University (A Central University) Vidya Vihar Raebareli Road U.P. Lucknow 226025 India

To address the drawbacks of existing secret data hiding techniques, such as their high computational demands for training deep neural networks or their limited hiding capacity, we present a new approach. Our proposed approach utilizes local binary pattern codes to generate hash sequences for concealing secret data, offering a more efficient solution. These hash sequences are used to encode the secret information within the cover images. To enhance security, we encrypt the mapping file using a Paillier cryptosystem. The recipient can successfully extract the secret information once the mapping file is decrypted and reverse the hash sequence generation algorithm is applied. The effectiveness of the method is demonstrated through extensive experimentation and comparison with the existing approaches. Results indicate that the proposed method not only reduces the computational cost but also outperforms traditional methods in terms of security and robustness. Our method reports approximately 95% accuracy in data recovery after common image processing attacks, such as scaling, noise addition, and image filtering, and allows to hide 72 bits of secret data per image. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Coverless information hiding Hiding capacity local binary pattern Paillier cryptosystem Robustness Security

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CS-CoLBP: Cross-Scale Co-occurrence local binary pattern for Image Classification

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INTERNATIONAL JOURNAL OF COMPUTER VISION 2025年第5期133卷 2327-2344页

作者： Xiao, Bin Shi, Danyu Bi, Xiuli Li, Weisheng Gao, Xinbo Chongqing Univ Posts & Telecommun Chongqing Peoples R China

The local binary pattern (LBP) is an effective feature, describing the size relationship between the neighboring pixels and the current pixel. While individual LBP-based methods yield good results, co-occurrence LBP-based methods exhibit a better ability to extract structural information. However, most of the co-occurrence LBP-based methods excel mainly in dealing with rotated images, exhibiting limitations in preserving performance for scaled images. To address the issue, a cross-scale co-occurrence LBP (CS-CoLBP) is proposed. Initially, we construct an LBP co-occurrence space to capture robust structural features by simulating scale transformation. Subsequently, we use Cross-Scale Co-occurrence pairs (CS-Co pairs) to extract the structural features, keeping robust descriptions even in the presence of scaling. Finally, we refine these CS-Co pairs through Rotation Consistency Adjustment (RCA) to bolster their rotation invariance, thereby making the proposed CS-CoLBP as powerful as existing co-occurrence LBP-based methods for rotated image description. While keeping the desired geometric invariance, the proposed CS-CoLBP maintains a modest feature dimension. Empirical evaluations across several datasets demonstrate that CS-CoLBP outperforms the existing state-of-the-art LBP-based methods even in the presence of geometric transformations and image manipulations.

关键词： Image classification local binary pattern Cross-scale Co-occurrence LBP-based features Scale invariant Rotation invariant

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A new texture descriptor based on hexagonal local binary pattern for content-based image retrieval

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DIGITAL SIGNAL PROCESSING 2025年 161卷

作者： Fadaei, Sadegh Azadimotlagh, Mehdi Rashno, Armin Beheshti, Amin Univ Yasuj Fac Engn Dept Elect Engn Yasuj Iran Persian Gulf Univ Jam Fac Engn Dept Comp Engn Bushehr Iran Lorestan Univ Fac Engn Dept Comp Engn Khorramabad Iran Macquarie Univ Sch Comp Sydney Australia

S R C Texture features play a vital role in content-based image retrieval (CBIR) applications. Most texture extraction methods have a low accuracy and high feature vector length. This paper presents a novel hexagonal local binary pattern (HLBP) to extract more informative and compact features from images. To have robust patterns against rotation, rotation invariant hexagonal patterns are presented using cyclic set theory. Texture feature vector is extracted from hexagonal images based on proposed patterns and used in CBIR application. To evaluate proposed method, experiments are performed in five datasets Corel-1k, Brodatz, VisTex, Corel-10k, and STex. The proposed HLBP method outperforms square local binary pattern (SLBP) in images with noise in the terms of precision. The feature vector length of the proposed method is 64, which is much shorter than those in competitive methods and leads to high speed in retrieval phase. The best performance of the proposed method is revealed in texture datasets which achieved the highest precision among all competitive methods.

关键词： local binary pattern Square lattice Hexagonal lattice Content-based image retrieval

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Decision-Level Fusion of Face and Gait Cues Using Fusion of Median local binary pattern (Median-LBP) and Gabor Scale Average (GSA)-Based PCA → LDA

Decision-Level Fusion of Face and Gait Cues Using Fusion of ...

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International Conference on Advances and Applications of Artificial Intelligence and Machine Learning, ICAAAIML 2023

作者： Annbuselvi, K. Santhi, N. Sivakumar, S. V. V. Vanniaperumal College for Women Tamil Nadu Virudhunagar India Cardamom Planter Association College Tamil Nadu Bodinayakanur India Madurai Kamaraj University Tamil Nadu Madurai India

ISBN: (纸本)9789819795062

The paper offers a methodology for recognizing humans by integrating the complementary features face, gait are extracted by fusion of Median local binary pattern (Median-LBP) and Gabor Scale Average (GSA)-based Principal Component Analysis followed by Linear Discriminant Analysis (PCA → LDA) at decision level is proposed. Firstly, the face features and gait features are extracted using Median-LBP and GSA. In Median-LBP, the center pixel value in a 3 × 3 window is computed using every 3 × 3 neighborhood, encompassing the central pixel. In GSA, initially, a filter bank is defined using Gabor Wavelet with three scales {1, 2, 3} and six angles {0, 30, 60, 90, 120, 150}. Next, a convolution operator is used for integrating the facial and gait cues with the defined filters, and the method averages six distinct angles of Gabor matrices with similar scaling into one average imaging called GSA. Then, they are enhanced via integrating Median-LBP and GSA feature vectors denoted as Median-LBP + GSA. Subsequently, lower dimensional feature vectors are obtained with the PCA → LDA algorithm. The Euclidean distance measure is utilized for obtaining the biometric system’s face, gait decisions independently, then the AND/OR rule is employed to fuse the decisions at the decision level for recognition. The methodology is tested with the openly available face dataset (ORL) and gait dataset (CASIA B). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： local binary pattern

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Leveraging Eager Learning for Retinal Abnormality Classification with local binary pattern Features

Leveraging Eager Learning for Retinal Abnormality Classifica...

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2025 International Conference on Advancement in Data Science, E-learning and Information System, ICADEIS 2025

作者： Silvanus Harianto, Eric Chandra Setiawan, Benny Yuda Prasetyo, Simeon Setyo Purwanto, Eko School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331513320

This study investigates the integration of local binary pattern (LBP) features with eager learning models for the classification of retinal abnormalities, focusing on diseases like Diabetic Retinopathy (DR) and Age-Related Macular Degeneration (AMD). Using the Retinal OCT Images (optical coherence tomography) dataset, we show that LBP is an effective technique for capturing the textural patterns associated with retinal conditions. Among various classifiers, the Random Forest model achieved the highest performance, with superior accuracy, recall, and F1 scores. This highlights the potential of LBP features combined with eager learning for robust and efficient classification of retinal diseases. The approach offers significant advantages for time-sensitive medical applications, supporting early predictions and real-time clinical workflows. Despite promising results, challenges such as addressing false positives and improving precision for overlapping conditions remain. Future work should explore deep learning models, hybrid feature extraction, and optimization strategies to enhance performance. Overall, this research demonstrates the potential for automated retinal disease detection systems, contributing to improved early diagnosis and better management of vision-threatening conditions. © 2025 IEEE.

关键词： local binary pattern

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Optimizing Oral Cancer Detection: A Hybrid Feature Fusion using local binary pattern and CNN

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Procedia Computer Science 2025年 258卷 476-486页

作者： Jeevan Kumar Vijay Pandey Rajesh Kumar Tiwari Jeevan Kumar Department of CSE JUT Ranchi 834010 India Vijay Pandey Department of ME BIT Sindri Dhanbad 828122 India Rajesh Kumar Tiwari Department of CSE RVSCET Jamshedpur 831012 India

One of the most hazardous diseases is cancer, which poses a significant threat to human health due to its ability to manifest in various parts of the body as abnormal, proliferative cells. In the case of oral cancer, diverse behavioral patterns may be observed, making early detection and accurate prognosis crucial for effective and timely treatment. Multi-Layer Visual Feature Fusion (MLVFF) is a novel feature fusion technique that is explor to develop a system to detect and predict oral cancer at its earliest, most treatable stages. MLVFF combines local binary patterns (LBP) characteristics with Convolutional Neural Network (CNN). More precisely, MLVFF may successfully improve the discriminating strength of features for Oral Cancer image recognition by combining the LBP and CNN versions, respectively. Test accuracy for the suggested model is 98.00%. In addition, we draw attention to emerging research questions in the field of cancer sickness, offering relevant data for further research that will improve cancer detection and treatments.

关键词： Oral cancer local binary pattern Convolutional neural network Artificial intelligence Deep Learning Machine Learning

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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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Person identification using novel local triangular binary pattern-based texture descriptor

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EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING 2025年第1期2025卷 1-14页

作者： Tekade, Arti Vijayan, T. Karthik, B. Mahajan, Anurag Bharath Inst Higher Educ & Res Dept Elect & Commun Engn Chennai India Pimpri Chinchwad Coll Engn & Res Coll Ravet Dept Elect & Tele Commun Pune India Symbiosis Int Symbiosis Inst Technol Dept Elect & Tele Commun Pune Campus Pune India

Human authentication is a crucial part of most computer vision automation systems. Conventional fingerprint, iris, face, or palm print-based systems cannot identify individuals when their external biometric components are destroyed, such as by severe burns, rashes, or wounds. The main elements of any person authentication system are non-forgery, security, resilience, and privacy. The local texture descriptor is vital in describing hand radiographic images' texture. This paper presents the novel local triangular binary pattern based texture descriptor to provide a local texture description of the hand radiographic images. The performance of the proposed descriptor is assessed using different machine learning classifiers such as K-nearest neighbor (KNN), support vector machine (SVM), radial basis function-SVM (RBF-SVM), classification tree (CT), and random forest (RF) for authentication of the 20 users based on hand radiographs. The suggested system provides an overall accuracy of 84.17% for KNN, 90% for SVM, 91.35% for RBF-SVM, 92.50% for CT, and 96.67% for RF for the 20 users for the In-house hand radiographic dataset.

关键词： Person authentication Forensic science Forensic hand radiography local binary pattern Support vector machine

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local binary pattern metric-based multi-focus image fusion

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OPTICS AND LASER TECHNOLOGY 2019年 110卷 62-68页

作者： Yin, Weiling Zhao, Wenda You, Di Wang, Dong Dalian Univ Technol Sch Informat & Commun Engn Dalian Peoples R China

Multi-focus image fusion is to integrate the partially focused images into one single image which is focused everywhere. Nowadays, it has become an important research topic due to the applications in more and more scientific fields. However, preserving more information of the low-contrast area in the focus area and maintaining the edge information are two challenges for existing approaches. In this paper, we address these two challenges with presenting a simple yet efficient multi-focus fusion method based on local binary pattern (LBP). In our algorithm, we measure the clarity using the LBP metric and construct the initial weight map. And then we use the connected area judgment strategy (CADS) to reduce the noise in the initial map. Afterwards, the two source images are fused together by weighted arranging. The experimental results validate that the proposed algorithm outperforms state-of-the-art image fusion algorithms in both qualitative and quantitative evaluations, especially when dealing with low contrast regions and edge information. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Multi-focus image fusion local binary pattern Connected area judgment strategy

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local binary pattern-based discriminant graph construction for dimensionality reduction with application to face recognition

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MULTIMEDIA TOOLS AND APPLICATIONS 2019年第16期78卷 22445-22462页

作者： Yang, Bo Li, Qian-zhong Inner Mongolia Univ Coll Comp Sci Hohhot 010021 Peoples R China Sch Phys Sci & Technol Hohhot 010021 Peoples R China Inner Mongolia Key Lab Data Min & Knowledge Engn Hohhot 010021 Peoples R China

Graph construction has attracted increasing interest in recent years due to its key role in many dimensionality reduction (DR) algorithms. On the other hand, our previous study shows that the local-binary-pattern Image (LBPI) representation is a more powerful discriminant and is invariant to monotonic gray level changes. Here, we attempt to construct a discriminant graph for DR in the LBPI representation space. We call the graph the local-binary-Image Discriminant (LBID) graph and further incorporate the LBID graph into the locality Preserving Projection (LPP) to develop an enhanced algorithm - local binary Image Discriminant Preserving Projection (LBIDPP). Meanwhile, we also construct a local-binary-Histogram (LBH) graph in LBP histogram space and obtain the local binary Histogram Preserving Projection (LBHPP) algorithm and compare these to the LBID graph and LBIDPP. It is worth noting that LBIDPP is not a simple combination of the two feature extractions LBP and LPP, i.e., LBP + LPP. LBIDPP inherits the attractive properties of the LBP and LPP. The experiments on face recognition validate the effectiveness and feasibility of the LBID graph and LBIDPP.

关键词： local binary pattern Dimensionality reduction Face recognition Graph embedding

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