Article
Computer Science, Information Systems
Umer Ali Khan, Ali Javed
Summary: The exponential growth of communal media platforms and the availability of low-cost digital capturing devices have led to a massive amount of multimedia content, particularly images. In this paper, the authors propose a novel approach, using LTAP to capture texture features and a genetic algorithm to select the best features for improved image retrieval performance. Experimental results demonstrate the effectiveness of the proposed method across multiple datasets.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
E. Rachdi, I. El Khadiri, Y. El merabet, Y. Rhazi, C. Meurie
Summary: This paper introduces a novel local feature extraction operator called MTSP, which is composed of two single-scale encoders, STP and SSP, designed based on a novel set theory pattern encoding scheme. Unlike other parametric texture operators, MTSP incorporates dynamic thresholds and can capture more detailed image information through the fusion of STP and SSP encoders. Experimental results demonstrate that MTSP achieves reliable performance stability on ten texture datasets and outperforms several representative methods in texture modeling, as verified by statistical tests.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2023)
Article
Engineering, Electrical & Electronic
Xinyu Lin, Yingjie Zhou, Xun Zhang, Yipeng Liu, Ce Zhu
Summary: Binary feature descriptors are widely used in visual measurement tasks, but they may not perform well for long-term visual measurement tasks due to their sensitivity to illumination variations. This study presents an illumination-insensitive binary (IIB) descriptor that leverages the local inter-patch invariance exhibited in multiple spatial granularities to deal with unfavorable illumination variations. Numerical experiments demonstrate that the proposed IIB descriptor outperforms state-of-the-art binary descriptors and some float descriptors.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Green & Sustainable Science & Technology
Saeed Aligholi, Reza Khajavi, Manoj Khandelwal, Danial Jahed Armaghani
Summary: In this paper, a rotation-invariant local binary pattern operator equipped with a local contrast measure is proposed to characterize the type of mineral twinning by inspecting the texture properties of crystals. The proposed system achieves high sensitivity and accuracy in texture identification and can be applied in various fields for classification and feature recognition tasks.
Article
Engineering, Electrical & Electronic
Shiqi Hu, Zhibin Pan, Jing Dong, Xincheng Ren
Summary: In this paper, a novel adaptively binarizing magnitude vector (ABMV) method is proposed to more accurately extract magnitude features in different directions of a texture image. The method calculates an average vector threshold adaptively and divides the texture image into smaller sub-images for improved classification accuracy and robustness to noise.
IEEE SIGNAL PROCESSING LETTERS
(2022)
Article
Engineering, Electrical & Electronic
Wei Huang, Yao Huang, Zebin Wu, Junru Yin, Qiqiang Chen
Summary: This article proposes a multikernel method based on a local binary pattern and random patches to improve classification accuracy of hyperspectral images. By combining local textural features, multilayer convolutional features, and spectral features, the proposed method achieves better classification performance compared to other methods on three HSI datasets.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2021)
Article
Computer Science, Artificial Intelligence
Qiwu Luo, Jiaojiao Su, Chunhua Yang, Olli Silven, Li Liu
Summary: In this paper, a novel image descriptor, called SNELBP, is proposed to address scale transformation and noise interference simultaneously. It achieves competitive results compared to classical LBP variants and typical deep learning methods.
PATTERN RECOGNITION
(2022)
Article
Mathematics, Applied
Martin Huska, Serena Morigi, Giuseppe Recupero
Summary: Geometric texture transfer is an advanced geometry modeling technique that adds fine-grained details to surfaces. It utilizes local geometric descriptors to represent the surface, preserving the global shape while transferring texture details. This approach is formulated as a constrained variational nonlinear optimization model and efficiently solved using numerical methods.
APPLIED MATHEMATICS AND COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Xin Shu, Hui Pan, Jinlong Shi, Xiaoning Song, Xiao-Jun Wu
Summary: This paper proposes a novel global refined local binary pattern (GRLBP) for texture feature extraction. By analyzing the nature of pixel intensity distribution in local neighborhoods, GRLBP can effectively describe and distinguish local neighborhoods with similar structures but different contrasts or grayscales. Experimental results demonstrate that GRLBP can represent detailed information of texture images and outperforms state-of-the-art LBP variants in terms of classification accuracy, feature dimension, and computational complexity.
PATTERN RECOGNITION
(2022)
Article
Computer Science, Information Systems
Usman Ali, Muhammad Tariq Mahmood
Summary: This paper proposes an effective blur measure based on local binary pattern (LBP) with adaptive threshold, and the evaluation using two datasets and comparison with other methods demonstrates that the proposed method performs significantly better in blur detection.
CMC-COMPUTERS MATERIALS & CONTINUA
(2022)
Article
Computer Science, Artificial Intelligence
Laleh Armi, Elham Abbasi, Jamal Zarepour-Ahmadabadi
Summary: This study introduces an improved feature extraction method (ILQP) and an ensemble learning-based classification method (MEETG). Experimental evaluation on texture classification shows that MEETG outperforms other methods in terms of classification accuracy.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Engineering, Electrical & Electronic
Tiecheng Song, Jie Feng, Lin Luo, Chenqiang Gao, Hongliang Li
Summary: In this paper, two novel operators, local grouped order pattern (LGOP) and non-local binary pattern (NLBP), are proposed for texture description. Experimental results demonstrate that combining LGOP and NLBP to construct discriminative histogram features as texture descriptor LGONBP shows superiority over state-of-the-art LBP variants for texture classification.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2021)
Article
Computer Science, Artificial Intelligence
Nuh Alpaslan
Summary: This paper presents novel hybrid methods based on neutrosophic set and LBP features. By transforming the input image into a neutrosophic domain and combining with grayscale images, the proposed methods can extract more robust features. The methods contribute to the classification performance with reasonable computational cost and achieve satisfactory results in experiments.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Information Systems
I. Michael Revina, W. R. Sam Emmanuel
Summary: This research proposes a method to remove noisy pixels from facial images, and utilizes various descriptors and classifiers for feature extraction and classification of facial expressions, with experimental results showing a good performance on two publicly available datasets.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2021)
Article
Engineering, Multidisciplinary
Youqi Zhang, Zhiyi Tang, Ruijing Yang
Summary: In this article, a method is proposed that combines multi-view local binary patterns and random forests to identify anomalous data in structural health monitoring systems. The method converts acceleration data into gray-scale image data, extracts texture features from the converted images, and uses random forests for prediction, accurately identifying multiple types of data anomalies.
Article
Biochemical Research Methods
Loris Nanni, Sheryl Brahnam, Stefano Ghidoni, Alessandra Lumini
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2019)
Article
Ecology
Alessandra Lumini, Loris Nanni
ECOLOGICAL INFORMATICS
(2019)
Article
Computer Science, Artificial Intelligence
Loris Nanni, Sheryl Brahnam, Alessandra Lumini
EXPERT SYSTEMS WITH APPLICATIONS
(2019)
Article
Computer Science, Artificial Intelligence
Loris Nanni, Alessandra Lumini, Federica Pasquali, Sheryl Brahnam
EXPERT SYSTEMS WITH APPLICATIONS
(2020)
Article
Chemistry, Analytical
Loris Nanni, Alessandra Lumini, Stefano Ghidoni, Gianluca Maguolo
Article
Chemistry, Multidisciplinary
Loris Nanni, Andrea Rigo, Alessandra Lumini, Sheryl Brahnam
APPLIED SCIENCES-BASEL
(2020)
Review
Computer Science, Artificial Intelligence
Alessandra Lumini, Loris Nanni
EXPERT SYSTEMS WITH APPLICATIONS
(2020)
Article
Chemistry, Multidisciplinary
Loris Nanni, Sheryl Brahnam, Alessandra Lumini, Gianluca Maguolo
APPLIED SCIENCES-BASEL
(2020)
Article
Chemistry, Analytical
Loris Nanni, Giovanni Minchio, Sheryl Brahnam, Gianluca Maguolo, Alessandra Lumini
Summary: The image classification system proposed in this study utilizes Siamese Neural Networks to generate dissimilarity spaces, calculates centroids with k-means clustering, and classifies images using SVMs. The system performs competitively on medical and animal audio data sets, achieving state-of-the-art performance without ad-hoc optimization of clustering methods on tested data sets.
Article
Chemistry, Analytical
Loris Nanni, Giovanni Minchio, Sheryl Brahnam, Davide Sarraggiotto, Alessandra Lumini
Summary: This paper examines strategies for enhancing the performance of ensembles of Siamese networks for image classification, utilizing different loss functions and methods for building dissimilarity spaces. Results demonstrate the robustness and versatility of the approach across various data sets, showing improved performance compared to standalone CNNs when combined in an ensemble.
Article
Imaging Science & Photographic Technology
Loris Nanni, Andrea Loreggia, Alessandra Lumini, Alberto Dorizza
Summary: Skin detection involves identifying skin and non-skin areas in a digital image and is commonly used in various applications. Recently, deep neural networks have had a major impact on the field of image segmentation detection, resulting in various successful models. This work surveys the most recent research in this field and proposes fair comparisons between approaches using different datasets.
JOURNAL OF IMAGING
(2023)
Article
Chemistry, Analytical
Loris Nanni, Carlo Fantozzi, Andrea Loreggia, Alessandra Lumini
Summary: In the field of computer vision, semantic segmentation involves identifying objects in images at the pixel level by classifying each pixel. This complex task requires advanced skills and context knowledge to accurately determine object boundaries. Semantic segmentation is crucial in various domains, such as medical diagnostics, where it simplifies early pathology detection. This study reviews literature on deep ensemble learning models for polyp segmentation and introduces new ensembles based on convolutional neural networks and transformers.
Article
Chemistry, Multidisciplinary
Loris Nanni, Sheryl Brahnam, Alessandra Lumini, Andrea Loreggia
Summary: Face detection is a vital task in computer vision, enabling applications such as facial recognition and human behavior analysis. This paper proposes a method to reduce false positives in face detection by utilizing depth map information. The evaluation demonstrates that the method effectively minimizes false positives without compromising detection rate, suggesting that incorporating depth information can enhance face detection accuracy.
APPLIED SCIENCES-BASEL
(2023)
Article
Imaging Science & Photographic Technology
Loris Nanni, Michelangelo Paci, Sheryl Brahnam, Alessandra Lumini
Summary: This study explores various data augmentation methods, introduces two novel approaches, and demonstrates their superior performance across four benchmark datasets, showing that varying data augmentation is a feasible way to build an ensemble of classifiers for image classification.
JOURNAL OF IMAGING
(2021)
Article
Imaging Science & Photographic Technology
Diego Baldissera, Loris Nanni, Sheryl Brahnam, Alessandra Lumini
Summary: Skin detectors are crucial in various applications, and this paper presents a new postprocessing method that enhances image classification performance by learning the application of different morphological sequences or homogeneity functions. The method is evaluated on multiple datasets, showing significant improvements in performance.
JOURNAL OF IMAGING
(2021)
Review
Computer Science, Artificial Intelligence
Wei Gao, Shuangshuang Ge
Summary: This study provides a comprehensive review of slope stability research based on artificial intelligence methods, focusing on slope stability computation and evaluation. The review covers studies using quasi-physical intelligence methods, simulated evolutionary methods, swarm intelligence methods, hybrid intelligence methods, artificial neural network methods, vector machine methods, and other intelligence methods. The merits, demerits, and state-of-the-art research advancement of these studies are analyzed, and possible research directions for slope stability investigation based on artificial intelligence methods are suggested.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Khuong Le Nguyen, Hoa Thi Trinh, Saeed Banihashemi, Thong M. Pham
Summary: This study investigated the influence of input parameters on the shear strength of RC squat walls and found that ensemble learning models, particularly XGBoost, can effectively predict the shear strength. The axial load had a greater influence than reinforcement ratio, and longitudinal reinforcement had a more significant impact compared to horizontal and vertical reinforcement. The performance of XGBoost model outperforms traditional design models and reducing input features still yields reliable predictions.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Bo Hu, Huiyan Zhang, Xiaoyi Wang, Li Wang, Jiping Xu, Qian Sun, Zhiyao Zhao, Lei Zhang
Summary: A deep hierarchical echo state network (DHESN) is proposed to address the limitations of shallow coupled structures. By using transfer entropy, candidate variables with strong causal relationships are selected and a hierarchical reservoir structure is established to improve prediction accuracy. Simulation results demonstrate that DHESN performs well in predicting algal bloom.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Limin Wang, Lingling Li, Qilong Li, Kuo Li
Summary: This paper discusses the urgency of learning complex multivariate probability distributions due to the increase in data variability and quantity. It introduces a highly scalable classifier called TAN, which utilizes maximum weighted spanning tree (MWST) for graphical modeling. The paper theoretically proves the feasibility of extending one-dependence MWST to model high-dependence relationships and proposes a heuristic search strategy to improve the fitness of the extended topology to data. Experimental results demonstrate that this algorithm achieves a good bias-variance tradeoff and competitive classification performance compared to other high-dependence or ensemble learning algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhejing Hu, Gong Chen, Yan Liu, Xiao Ma, Nianhong Guan, Xiaoying Wang
Summary: Anxiety is a prevalent issue and music therapy has been found effective in reducing anxiety. To meet the diverse needs of individuals, a novel model called the spatio-temporal therapeutic music transfer model (StTMTM) is proposed.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Nur Ezlin Zamri, Mohd. Asyraf Mansor, Mohd Shareduwan Mohd Kasihmuddin, Siti Syatirah Sidik, Alyaa Alway, Nurul Atiqah Romli, Yueling Guo, Siti Zulaikha Mohd Jamaludin
Summary: In this study, a hybrid logic mining model was proposed by combining the logic mining approach with the Modified Niche Genetic Algorithm. This model improves the generalizability and storage capacity of the retrieved induced logic. Various modifications were made to address other issues. Experimental results demonstrate that the proposed model outperforms baseline methods in terms of accuracy, precision, specificity, and correlation coefficient.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
David Jacob Kedziora, Tien-Dung Nguyen, Katarzyna Musial, Bogdan Gabrys
Summary: The paper addresses the problem of efficiently optimizing machine learning solutions by reducing the configuration space of ML pipelines and leveraging historical performance. The experiments conducted show that opportunistic/systematic meta-knowledge can improve ML outcomes, and configuration-space culling is optimal when balanced. The utility and impact of meta-knowledge depend on various factors and are crucial for generating informative meta-knowledge bases.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
G. Sophia Jasmine, Rajasekaran Stanislaus, N. Manoj Kumar, Thangamuthu Logeswaran
Summary: In the context of a rapidly expanding electric vehicle market, this research investigates the ideal locations for EV charging stations and capacitors in power grids to enhance voltage stability and reduce power losses. A hybrid approach combining the Fire Hawk Optimizer and Spiking Neural Network is proposed, which shows promising results in improving system performance. The optimization approach has the potential to enhance the stability and efficiency of electric grids.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhijiang Wu, Guofeng Ma
Summary: This study proposes a natural language processing-based framework for requirement retrieval and document association, which can help to mine and retrieve documents related to project managers' requirements. The framework analyzes the ontology relevance and emotional preference of requirements. The results show that the framework performs well in terms of iterations and threshold, and there is a significant matching between the retrieved documents and the requirements, which has significant managerial implications for construction safety management.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Yung-Kuan Chan, Chuen-Horng Lin, Yuan-Rong Ben, Ching-Lin Wang, Shu-Chun Yang, Meng-Hsiun Tsai, Shyr-Shen Yu
Summary: This study proposes a novel method for dog identification using nose-print recognition, which can be applied to controlling stray dogs, locating lost pets, and pet insurance verification. The method achieves high recognition accuracy through two-stage segmentation and feature extraction using a genetic algorithm.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Shaohua Song, Elena Tappia, Guang Song, Xianliang Shi, T. C. E. Cheng
Summary: This study aims to optimize supplier selection and demand allocation decisions for omni-channel retailers in order to achieve supply chain resilience. It proposes a two-phase approach that takes into account various factors such as supplier evaluation and demand allocation.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jinyan Hu, Yanping Jiang
Summary: This paper examines the allocation problem of shared parking spaces considering parking unpunctuality and no-shows. It proposes an effective approach using sample average approximation (SAA) combined with an accelerating Benders decomposition (ABD) algorithm to solve the problem. The numerical experiments demonstrate the significance of supply-demand balance for the operation and user satisfaction of the shared parking system.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Soroor Motie, Bijan Raahemi
Summary: Financial fraud is a persistent problem in the finance industry, but Graph Neural Networks (GNNs) have emerged as a powerful tool for detecting fraudulent activities. This systematic review provides a comprehensive overview of the current state-of-the-art technologies in using GNNs for financial fraud detection, identifies gaps and limitations in existing research, and suggests potential directions for future research.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Enhao Ning, Changshuo Wang, Huang Zhang, Xin Ning, Prayag Tiwari
Summary: This review provides a detailed overview of occluded person re-identification methods and conducts a systematic analysis and comparison of existing deep learning-based approaches. It offers important theoretical and practical references for future research in the field.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jiajun Ma, Songyu Hu, Jianzhong Fu, Gui Chen
Summary: The article presents a novel visual hierarchical attention detector for multi-scale defect location and classification, utilizing texture, semantic, and instance features of defects through a hierarchical attention mechanism, achieving multi-scale defect detection in bearing images with complex backgrounds.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)