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Journal articles on the topic 'Global Feature'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Arman, A. C., and G. M. Boynton. "Feature specificity of global-feature-based-attention." Journal of Vision 5, no. 8 (2010): 159. http://dx.doi.org/10.1167/5.8.159.

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2

Zirnsak, M., and F. Hamker. "Global feature-based attention distorts feature space." Journal of Vision 10, no. 7 (2010): 190. http://dx.doi.org/10.1167/10.7.190.

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3

Wang, Chenchen, Jun Wang, Yanfei Li, Chengkai Piao, and Jinmao Wei. "Dual-Regularized Feature Selection for Class-Specific and Global Feature Associations." Entropy 27, no. 2 (2025): 190. https://doi.org/10.3390/e27020190.

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Understanding feature associations is vital for selecting the most informative features. Existing methods primarily focus on global feature associations, which capture overall relationships across all samples. However, they often overlook class-specific feature interactions, which are essential for capturing locality features that may only be significant within certain classes. In this paper, we propose Dual-Regularized Feature Selection (DRFS), which incorporates two feature association regularizers to address both class-specific and global feature relationships. The class-specific regularize

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Li, Tian Wen, and Yun Gao. "Object Representation Fusing Global and Local Features." Applied Mechanics and Materials 373-375 (August 2013): 1022–26. http://dx.doi.org/10.4028/www.scientific.net/amm.373-375.1022.

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In the actual complex scenes, multi-feature fusion has become a valid method of object representation for tracking video motion targets. Two keys about multi-feature fusion are how to select some valid features and how to fuse the features. In this paper, we propose an object representation fusing global and local features for object tracking. In our method, we select a common hue histogram as the global feature and use a valid SIFT feature as the local feature. In the tracking frame of particle filter, the tracking results show that our proposed object representation can better restrain the d

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Zhang, Lin, Hua Meng, Yunbing Yan, and Xiaowei Xu. "Transformer-Based Global PointPillars 3D Object Detection Method." Electronics 12, no. 14 (2023): 3092. http://dx.doi.org/10.3390/electronics12143092.

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The PointPillars algorithm can detect vehicles, pedestrians, and cyclists on the road, and is widely used in the field of environmental awareness in autonomous driving. However, its feature encoding network only uses a minimalist PointNet network for feature extraction of point cloud information, which does not consider the global context information of the point cloud, and the local structure features are not sufficiently extracted, and these feature losses can seriously affect the performance of the object detection network. To address this problem, this paper proposes an improved PointPilla

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BUCHALA, SAMARASENA, NEIL DAVEY, RAY J. FRANK, MARTIN LOOMES, and TIM M. GALE. "THE ROLE OF GLOBAL AND FEATURE BASED INFORMATION IN GENDER CLASSIFICATION OF FACES: A COMPARISON OF HUMAN PERFORMANCE AND COMPUTATIONAL MODELS." International Journal of Neural Systems 15, no. 01n02 (2005): 121–28. http://dx.doi.org/10.1142/s0129065705000074.

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Most computational models for gender classification use global information (the full face image) giving equal weight to the whole face area irrespective of the importance of the internal features. Here, we use a global and feature based representation of face images that includes both global and featural information. We use dimensionality reduction techniques and a support vector machine classifier and show that this method performs better than either global or feature based representations alone. We also present results of human subjects performance on gender classification task and evaluate

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Muralitharan, Morley, Sebastian Agricola, Mukunth Manickavasagam, and Chris Gray. "Feature." Asia-Pacific Biotech News 14, no. 04 (2010): 19–22. http://dx.doi.org/10.1142/s0219030310000194.

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8

Shao, Fan, Kai Wang, and Yanluo Liu. "Salient object detection algorithm based on diversity features and global guidance information." Innovation & Technology Advances 1, no. 1 (2023): 12–20. http://dx.doi.org/10.61187/ita.v1i1.14.

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Aiming at the problems of traditional salient object detection methods such as fuzzy boundary and insufficient information integrity, a salient object detection network composed of feature diversity enhancement module, global information guidance module and feature fusion module is proposed. Firstly, asymmetric convolution, cavity convolution and common convolution are spliced to form a feature diversity enhancement module to extract different types of spatial features corresponding to each feature layer. Secondly, the global information guidance module transmits the information captured by th

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Zaidan, Noor Aina, and Md Sah Hj Salam. "Emotional speech feature selection using end-part segmented energy feature." Indonesian Journal of Electrical Engineering and Computer Science 15, no. 3 (2019): 1374. http://dx.doi.org/10.11591/ijeecs.v15.i3.pp1374-1381.

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The accuracy of human emotional detection is crucial in the industry to ensure effective conversations and messages delivery. The process involved in identifying emotions must be carried out properly and using a method that guarantees high level of emotional recognition. Energy feature is said to be a prosodic information encoder and there are still studies on energy use in speech prosody and it motivate us to run an experiment on energy features. We have conducted two sets of studies: 1) whether local or global features that contribute most to emotional recognition and 2) the effect of the en

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Xu, Zeyu, Cheng Su, Shirou Wang, and Xiaocan Zhang. "Local and Global Spectral Features for Hyperspectral Image Classification." Remote Sensing 15, no. 7 (2023): 1803. http://dx.doi.org/10.3390/rs15071803.

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Hyperspectral images (HSI) contain powerful spectral characterization capabilities and are widely used especially for classification applications. However, the rich spectrum contained in HSI also increases the difficulty of extracting useful information, which makes the feature extraction method significant as it enables effective expression and utilization of the spectrum. Traditional HSI feature extraction methods design spectral features manually, which is likely to be limited by the complex spectral information within HSI. Recently, data-driven methods, especially the use of convolutional

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11

S., Ramadevi* B.Soundarya. "TECHNIQUES OF FEATURE EXTRACTION USING LOCAL AND GLOBAL FEATURES: A SURVEY." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 3 (2017): 51–56. https://doi.org/10.5281/zenodo.345693.

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An image hash is a short code that holds the content of the image. Image authentication using hash code is most commonly used technique. This image hash can be generated using many techniques. The major drawback of image hash is the possibility to have the same hash code for two different images. However, this vulnerability can be greatly reduced by generating hash code that uses both local and global features. In this paper, a survey on some feature extraction technique using local and global features is given. The advantages and disadvantages of each and every system along with future work i

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Tao, Sha, and Zhenfeng Wang. "Differential Feature Fusion, Triplet Global Attention, and Web Semantic for Pedestrian Detection." International Journal on Semantic Web and Information Systems 20, no. 1 (2024): 1–18. http://dx.doi.org/10.4018/ijswis.345651.

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In complex environments and crowded pedestrian scenes, the overlap or loss of local features is a pressing issue. However, existing methods often struggle to strike a balance between eliminating interfering features and establishing feature connections. To address this challenge, we introduce a novel pedestrian detection approach called Differential Feature Fusion under Triplet Global Attention (DFFTGA). This method merges feature maps of the same size from different stages to introduce richer feature information. Specifically, we introduce a pixel-level Triplet Global Attention (TGA) module t

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Liu, T., and Y. Hou. "Global feature-based attention to orientation." Journal of Vision 11, no. 10 (2011): 8. http://dx.doi.org/10.1167/11.10.8.

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14

Rocco, Vincent A. "FEATURE: Going Global: A CEO's Perspective." Journal of Management in Engineering 12, no. 2 (1996): 21–24. http://dx.doi.org/10.1061/(asce)0742-597x(1996)12:2(21).

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15

Wang, De, Feiping Nie, and Heng Huang. "Feature Selection via Global Redundancy Minimization." IEEE Transactions on Knowledge and Data Engineering 27, no. 10 (2015): 2743–55. http://dx.doi.org/10.1109/tkde.2015.2426703.

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Mori, Minoru, Seiichi Uchida, and Hitoshi Sakano. "Global feature for online character recognition." Pattern Recognition Letters 35 (January 2014): 142–48. http://dx.doi.org/10.1016/j.patrec.2013.03.036.

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Gao, Suining, Xiubin Yang, Li Jiang, Zongqiang Fu, and Jiamin Du. "Global feature-based multimodal semantic segmentation." Pattern Recognition 151 (July 2024): 110340. http://dx.doi.org/10.1016/j.patcog.2024.110340.

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18

Wang, Wenbiao, Qianqian Zhang, and Youwei Hao. "Fault Detection Based on Kernel Global Local Preserving Projection." Information 16, no. 4 (2025): 256. https://doi.org/10.3390/info16040256.

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In this paper, a fault detection method based on kernel global local preserving projection is presented to address the nonlinear characteristics of industrial systems. First, data are projected into a high-dimensional feature space through nonlinear mapping, enabling linear separability in this feature space. Subsequently, data features are extracted using the global local preserving projection method in the high-dimensional feature space. Finally, a monitoring model is established based on these features. Experiments on the Tennessee Eastman process and industrial boilers demonstrate that the

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Li, Yunfei, Hao Liu, Jiuzhen Liang, and Daihong Jiang. "Occlusion-Robust Facial Expression Recognition Based on Multi-Angle Feature Extraction." Applied Sciences 15, no. 9 (2025): 5139. https://doi.org/10.3390/app15095139.

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Facial occlusion represents a significant challenge in the domain of facial expression recognition (FER). The absence of feature information due to occlusion has been demonstrated to result in a reduction in recognition accuracy and model robustness. To address this challenge, a multi-angle feature extraction (MAFE) method is proposed in this paper, aiming to enhance the recognition accuracy under occlusion conditions by employing multi-scale global features, local fine-grained features, and important regional features. The MAFE approach involves three core modules: multi-feature extraction, r

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Wang, Shanfeng, Jianzhao Li, Zaitian Liu, Yourun Zhang, and Maoguo Gong. "FedFSL-CFRD: Personalized Federated Few-Shot Learning with Collaborative Feature Representation Disentanglement." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 20 (2025): 21243–51. https://doi.org/10.1609/aaai.v39i20.35423.

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Federated few-shot learning (FedFSL) aims to enable the clients to obtain personalized generalization models for unseen categories with only a small number of referenceable samples in the distributed collaborative training paradigm. Most existing FedFSL-related algorithms suffer from domain bias and feature coupling in the presence of data heterogeneity and sample scarcity. In this work, we propose a collaborative feature representation disentanglement (CFRD) scheme for FedFSL to address these issues. After each client receives the global aggregation parameters, the original feature representa

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Wu, Shaohua, Yong Hu, Wei Wang, Xinyong Feng, and Wanneng Shu. "Application of Global Optimization Methods for Feature Selection and Machine Learning." Mathematical Problems in Engineering 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/241517.

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The feature selection process constitutes a commonly encountered problem of global combinatorial optimization. The process reduces the number of features by removing irrelevant and redundant data. This paper proposed a novel immune clonal genetic algorithm based on immune clonal algorithm designed to solve the feature selection problem. The proposed algorithm has more exploration and exploitation abilities due to the clonal selection theory, and each antibody in the search space specifies a subset of the possible features. Experimental results show that the proposed algorithm simplifies the fe

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Huang, Cuiyang, and Zihan Hu. "A multimodal transformer-based visual question answering method integrating local and global information." PLOS One 20, no. 7 (2025): e0324757. https://doi.org/10.1371/journal.pone.0324757.

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Addressing the limitations in current visual question answering (VQA) models face limitations in multimodal feature fusion capabilities and often lack adequate consideration of local information, this study proposes a multimodal Transformer VQA network based on local and global information integration (LGMTNet). LGMTNet employs attention on local features within the context of global features, enabling it to capture both broad and detailed image information simultaneously, constructing a deep encoder-decoder module that directs image feature attention based on the question context, thereby enh

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Dhar, Prashengit, Md Shohelur Rahman, and Zainal Abedin. "Classification of Leaf Disease Using Global and Local Features." International Journal of Information Technology and Computer Science 14, no. 1 (2022): 43–57. http://dx.doi.org/10.5815/ijitcs.2022.01.05.

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Leaf disease of plants causes great loss in productivity of crops. So proper take care of plants is mandatory. Plants can be affected by various diseases. So Early diagnosis of leaf disease is a good practice. Computer vision-based classification of leaf disease can be a great way in diagnosing diseases early. Early detection of diseases can lead to better treatment. Vision based technology can identify disease quickly. Though deep learning is trending and using vastly for recognition task, but it needs very large dataset and also consumes much time. This paper introduced a method to classify

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Ye, Qing, and Yaxin Sun. "Global Structure Preservation and Self-Representation-Based Supervised Feature Selection." International Journal of Cognitive Informatics and Natural Intelligence 18, no. 1 (2024): 1–19. http://dx.doi.org/10.4018/ijcini.346987.

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Feature selection aims to select a subset of features from high-dimensional data, which can overcome the curse of dimensionality for the next dealing steps. However, the feature selection itself could face the curse of dimensionality. To overcome the above problem, in this paper, a new feature selection framework is designed according to a human processing in our daily life. In our daily life, to evaluate a candidate's ability to work, the related professional knowledge and the comprehensive ability of a candidate should be both evaluated. Actually, a candidate only with good professional know

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SOUNDAR, K. RUBA, and K. MURUGESAN. "CORRELATION BASED FACE MATCHING IN COMBINED GLOBAL AND LOCAL PRESERVING FEATURE SPACE." International Journal of Pattern Recognition and Artificial Intelligence 24, no. 08 (2010): 1281–94. http://dx.doi.org/10.1142/s021800141000838x.

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Face recognition plays a vital role in authentication, monitoring, indexing, access control and other surveillance applications. Much research on face recognition with various feature based approaches using global or local features employing a number of similarity measurement techniques have been done earlier. Feature based approaches using global features can effectively preserve only the Euclidean structure of face space, that suffer from lack of local features which may play a major role in some applications. On the other hand, wtih local features only the face subspace that best detects th

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Li, Cuixia, Shanshan Yang, Li Shi, Yue Liu, and Yinghao Li. "PTRNet: Global Feature and Local Feature Encoding for Point Cloud Registration." Applied Sciences 12, no. 3 (2022): 1741. http://dx.doi.org/10.3390/app12031741.

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Existing end-to-end cloud registration methods are often inefficient and susceptible to noise. We propose an end-to-end point cloud registration network model, Point Transformer for Registration Network (PTRNet), that considers local and global features to improve this behavior. Our model uses point clouds as inputs and applies a Transformer method to extract their global features. Using a K-Nearest Neighbor (K-NN) topology, our method then encodes the local features of a point cloud and integrates them with the global features to obtain the point cloud’s strong global features. Comparative ex

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Odoyo, Wilfred O., and Beom-Joon Cho. "Global Feature Extraction and Recognition from Matrices of Gabor Feature Faces." Journal of information and communication convergence engineering 9, no. 2 (2011): 207–11. http://dx.doi.org/10.6109/jicce.2011.9.2.207.

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Bell, J., M. Forsyth, D. R. Badcock, and F. A. A. Kingdom. "Global shape processing involves feature-selective and feature-agnostic coding mechanisms." Journal of Vision 14, no. 11 (2014): 12. http://dx.doi.org/10.1167/14.11.12.

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Huang, Xin, Yunfeng Bai, Dong Liang, Feng Tian, and Jinyuan Jia. "G2L-CariGAN: Caricature Generation from Global Structure to Local Features." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 3 (2024): 2391–99. http://dx.doi.org/10.1609/aaai.v38i3.28014.

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Existing GAN-based approaches to caricature generation mainly focus on exaggerating a character’s global facial structure. This often leads to the failure in highlighting significant facial features such as big eyes and hook nose. To address this limitation, we propose a new approach termed as G2L-CariGAN, which uses feature maps of spatial dimensions instead of latent codes for geometric exaggeration. G2L-CariGAN first exaggerates the global facial structure of the character on a low-dimensional feature map and then exaggerates its local facial features on a high-dimensional feature map. More

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Xie, Jun, Wentian Xin, Ruyi Liu, et al. "Global Co-Occurrence Feature and Local Spatial Feature Learning for Skeleton-Based Action Recognition." Entropy 22, no. 10 (2020): 1135. http://dx.doi.org/10.3390/e22101135.

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Recent progress on skeleton-based action recognition has been substantial, benefiting mostly from the explosive development of Graph Convolutional Networks (GCN). However, prevailing GCN-based methods may not effectively capture the global co-occurrence features among joints and the local spatial structure features composed of adjacent bones. They also ignore the effect of channels unrelated to action recognition on model performance. Accordingly, to address these issues, we propose a Global Co-occurrence feature and Local Spatial feature learning model (GCLS) consisting of two branches. The f

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Wang, Shuai, Genwen Fang, Lei Liu, Jun Wang, Kongfen Zhu, and Silas N. Melo. "Transformer-Based Visual Object Tracking with Global Feature Enhancement." Applied Sciences 13, no. 23 (2023): 12712. http://dx.doi.org/10.3390/app132312712.

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With the rise of general models, transformers have been adopted in visual object tracking algorithms as feature fusion networks. In these trackers, self-attention is used for global feature enhancement. Cross-attention is applied to fuse the features of the template and the search regions to capture the global information of the object. However, studies have found that the feature information fused by cross-attention does not pay enough attention to the object region. In order to enhance cross-attention for the object region, an enhanced cross-attention (ECA) module is proposed for global feat

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Lai, Hefeng, and Peng Zhang. "Few-Shot Object Detection with Local Feature Enhancement and Feature Interrelation." Electronics 12, no. 19 (2023): 4036. http://dx.doi.org/10.3390/electronics12194036.

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Few-shot object detection (FSOD) aims at designing models that can accurately detect targets of novel classes in a scarce data regime. Existing research has improved detection performance with meta-learning-based models. However, existing methods continue to exhibit certain imperfections: (1) Only the interacting global features of query and support images lead to ignoring local critical features in the imprecise localization of objects from new categories. (2) Convolutional neural networks (CNNs) encounter difficulty in learning diverse pose features from exceedingly limited labeled samples o

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Lin, Chih-Wei, Mengxiang Lin, and Jinfu Liu. "Object–Part Registration–Fusion Net for Fine-Grained Image Classification." Symmetry 13, no. 10 (2021): 1838. http://dx.doi.org/10.3390/sym13101838.

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Classifying fine-grained categories (e.g., bird species, car, and aircraft types) is a crucial problem in image understanding and is difficult due to intra-class and inter-class variance. Most of the existing fine-grained approaches individually utilize various parts and local information of objects to improve the classification accuracy but neglect the mechanism of the feature fusion between the object (global) and object’s parts (local) to reinforce fine-grained features. In this paper, we present a novel framework, namely object–part registration–fusion Net (OR-Net), which considers the mec

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Zhao, Hongwei, Jiaxin Wu, Danyang Zhang, and Pingping Liu. "Toward Improving Image Retrieval via Global Saliency Weighted Feature." ISPRS International Journal of Geo-Information 10, no. 4 (2021): 249. http://dx.doi.org/10.3390/ijgi10040249.

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For full description of images’ semantic information, image retrieval tasks are increasingly using deep convolution features trained by neural networks. However, to form a compact feature representation, the obtained convolutional features must be further aggregated in image retrieval. The quality of aggregation affects retrieval performance. In order to obtain better image descriptors for image retrieval, we propose two modules in our method. The first module is named generalized regional maximum activation of convolutions (GR-MAC), which pays more attention to global information at multiple

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Kar, Biswajit, Bhimraj Prasai Chetry, and Sayan Das. "Feature Ranking Using Novel Consistency Measure by Normalized Standard Deviation and Proposal of Three Novel Global Features for Online Signature Verification." International Journal of Experimental Research and Review 44 (October 30, 2024): 185–95. http://dx.doi.org/10.52756/ijerr.2024.v44spl.016.

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Signature is a behavioral biometric that evolves throughout a person’s life. Feature extraction and ranking are very important steps towards online signature verification in order to achieve high efficiency. In our case, we have extracted 48 global features. A novel feature ranking technique based on consistency measure using normalized standard deviation is proposed here and is compared with well-established mRMR based ranking. The use of normalized standard deviation formula is the novel approach for feature ranking. Moreover, we have proposed three novel global features for consistency meas

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Wijaya, Kevin Tirta, Dong-Hee Paek, and Seung-Hyun Kong. "Advanced Feature Learning on Point Clouds Using Multi-Resolution Features and Learnable Pooling." Remote Sensing 16, no. 11 (2024): 1835. http://dx.doi.org/10.3390/rs16111835.

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Existing point cloud feature learning networks often learn high-semantic point features representing the global context by incorporating sampling, neighborhood grouping, neighborhood-wise feature learning, and feature aggregation. However, this process may result in a substantial loss of granular information due to the sampling operation and the widely-used max pooling feature aggregation, which neglects information from non-maximum point features. Consequently, the resulting high-semantic point features could be insufficient to represent the local context, hindering the network’s ability to d

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Da, Zikai, Yu Gao, Zihan Xue, Jing Cao, and Peizhen Wang. "Local and Global Feature Aggregation-Aware Network for Salient Object Detection." Electronics 11, no. 2 (2022): 231. http://dx.doi.org/10.3390/electronics11020231.

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With the rise of deep learning technology, salient object detection algorithms based on convolutional neural networks (CNNs) are gradually replacing traditional methods. The majority of existing studies, however, focused on the integration of multi-scale features, thereby ignoring the characteristics of other significant features. To address this problem, we fully utilized the features to alleviate redundancy. In this paper, a novel CNN named local and global feature aggregation-aware network (LGFAN) has been proposed. It is a combination of the visual geometry group backbone for feature extra

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Xi, Suyang, Yunhao Liu, Hong Ding, Mingshuo Wang, Zhenghan Chen, and Xiaoxuan Liang. "GEONet: Global Enhancement and Optimization Network for Lane Detection." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 8 (2025): 8559–66. https://doi.org/10.1609/aaai.v39i8.32924.

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Lane detection plays a crucial role in autonomous driving systems, enabling vehicles to navigate safely and efficiently in complex environment. Despite significant advancements in recent years, accurate lane detection remains a challenging task, particularly in scenarios with occlusions, ambiguous lane markings, and diverse lighting conditions. In this paper, we propose the Global Enhancement and Optimization Network (GEONet) for lane detection, which is designed to refine both feature extraction and global feature transmission. Traditional approaches typically depend on deep convolutional lay

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Liu, Qian, Zebin Wu, Xiuping Jia, Yang Xu, and Zhihui Wei. "From Local to Global: Class Feature Fused Fully Convolutional Network for Hyperspectral Image Classification." Remote Sensing 13, no. 24 (2021): 5043. http://dx.doi.org/10.3390/rs13245043.

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Current mainstream networks for hyperspectral image (HSI) classification employ image patches as inputs for feature extraction. Spatial information extraction is limited by the size of inputs, which makes networks unable to perform effective learning and reasoning from the global perspective. As a common component for capturing long-range dependencies, non-local networks with pixel-by-pixel information interaction bring unaffordable computational costs and information redundancy. To address the above issues, we propose a class feature fused fully convolutional network (CFF-FCN) with a local fe

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Xu, Wang, Renwen Chen, Bin Huang, Xiang Zhang, and Chuan Liu. "Single Image Super-Resolution Based on Global Dense Feature Fusion Convolutional Network." Sensors 19, no. 2 (2019): 316. http://dx.doi.org/10.3390/s19020316.

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Deep neural networks (DNNs) have been widely adopted in single image super-resolution (SISR) recently with great success. As a network goes deeper, intermediate features become hierarchical. However, most SISR methods based on DNNs do not make full use of the hierarchical features. The features cannot be read directly by the subsequent layers, therefore, the previous hierarchical information has little influence on the subsequent layer output, and the performance is relatively poor. To address this issue, a novel global dense feature fusion convolutional network (DFFNet) is proposed, which can

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Wanzhen Wang, Sze Song Ngu, Miaomiao Xin, et al. "Tool Wear Prediction Combining Global Feature Attention and Long Short-Term Memory Network." Proceedings of Engineering and Technology Innovation 28 (October 17, 2024): 01–14. http://dx.doi.org/10.46604/peti.2024.14201.

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This study aims to accurately predict tool flank wear in milling and simplify the complexity of feature selection. A hybrid approach is proposed to eclectically integrate the advantages between the long short-term memory (LSTM) network and the global feature attention (GFA) module. First, the feature matrix is calculated using the multi-domain feature extraction method. Subsequently, a parallel network is employed to achieve feature fusion. The stacked LSTM network extracts the temporal dependencies between features and the GFA module is used to adaptively complement key features representing

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Li, Yang, Fangyuan Ma, Cheng Ji, Jingde Wang, and Wei Sun. "Fault Detection Method Based on Global-Local Marginal Discriminant Preserving Projection for Chemical Process." Processes 10, no. 1 (2022): 122. http://dx.doi.org/10.3390/pr10010122.

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Feature extraction plays a key role in fault detection methods. Most existing methods focus on comprehensive and accurate feature extraction of normal operation data to achieve better detection performance. However, discriminative features based on historical fault data are usually ignored. Aiming at this point, a global-local marginal discriminant preserving projection (GLMDPP) method is proposed for feature extraction. Considering its comprehensive consideration of global and local features, global-local preserving projection (GLPP) is used to extract the inherent feature of the data. Then,

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CAI Qiang, 蔡. 强., 郝佳云 HAO Jia-yun, 曹. 健. CAO Jian, and 李海生 LI Hai-sheng. "Salient detection via local and global feature." Optics and Precision Engineering 25, no. 3 (2017): 772–78. http://dx.doi.org/10.3788/ope.20172503.0772.

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Taher Azar, Ahmad, Zafar Iqbal Khan, Syed Umar Amin, and Khaled M. Fouad. "Hybrid Global Optimization Algorithm for Feature Selection." Computers, Materials & Continua 74, no. 1 (2023): 2021–37. http://dx.doi.org/10.32604/cmc.2023.032183.

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Bondarenko, R., C. N. Boehler, C. M. Stoppel, H. J. Heinze, M. A. Schoenfeld, and J. M. Hopf. "Separable Mechanisms Underlying Global Feature-Based Attention." Journal of Neuroscience 32, no. 44 (2012): 15284–95. http://dx.doi.org/10.1523/jneurosci.1132-12.2012.

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Aho, Alfred V., and Nancy D. Griffeth. "Feature interactions in the global information infrastructure." ACM SIGSOFT Software Engineering Notes 20, no. 4 (1995): 2–4. http://dx.doi.org/10.1145/222132.222133.

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Phillips, F., and J. T. Todd. "Texture discrimination based on global feature alignments." Journal of Vision 10, no. 6 (2010): 6. http://dx.doi.org/10.1167/10.6.6.

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Lin, Mingqiang, Chenbin Zhang, and Zonghai Chen. "Global feature integration based salient region detection." Neurocomputing 159 (July 2015): 1–8. http://dx.doi.org/10.1016/j.neucom.2015.02.050.

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Shinitzky, Meir. "Space Asymmetry as a Possible Global Feature." Chirality 25, no. 5 (2013): 308–11. http://dx.doi.org/10.1002/chir.22152.

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JIANG, Xin, Haitao NIE, and Ming ZHU. "Global and local feature fusion image dehazing." Optics and Precision Engineering 31, no. 18 (2023): 2687–99. http://dx.doi.org/10.37188/ope.20233118.2687.

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