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arXiv 2024年

作者： Li, Hongwei Bran Navarro, Fernando Ezhov, Ivan Bayat, Amirhossein Das, Dhritiman Kofler, Florian Shit, Suprosanna Waldmannstetter, Diana Paetzold, Johannes C. Hu, Xiaobin Wiestler, Benedikt Zimmer, Lucas Amiranashvili, Tamaz Prabhakar, Chinmay Berger, Christoph Weidner, Jonas Alonso-Basanta, Michelle Rashid, Arif Baid, Ujjwal Adel, Wesam Alis, Deniz Baheti, Bhakti Bai, Yingbin Bhat, Ishaan Cetindag, Sabri Can Chen, Wenting Cheng, Li Dutande, Prasad Dular, Lara Elattar, Mustafa A. Feng, Ming Gao, Shengbo Huisman, Henkjan Hu, Weifeng Innani, Shubham Ji, Wei Karimi, Davood Kuijf, Hugo J. Kwak, Jin Tae Le, Hoang Long Li, Xiang Lin, Huiyan Liu, Tongliang Ma, Jun Ma, Kai Ma, Ting Oksuz, Ilkay Holland, Robbie Oliveira, Arlindo L. Pal, Jimut Bahan Pei, Xuan Qiao, Maoying Saha, Anindo Selvan, Raghavendra Shen, Linlin Silva, Joao Lourenco Spiclin, Ziga Talbar, Sanjay Wang, Dadong Wang, Wei Wang, Xiong Wang, Yin Xi, Ruiling Xu, Kele Yang, Yanwu Yergin, Mert Yu, Shuang Zeng, Lingxi Zhang, YingLin Zhao, Jiachen Zheng, Yefeng Zukovec, Martin Do, Richard Becker, Anton Simpson, Amber Konukoglu, Ender Jakab, Andras Bakas, Spyridon Joskowicz, Leo Menze, Bjoern Department of Informatics Technical University of Munich Germany Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Harvard Medical School United States Department of Quantitative Biomedicine University of Zurich Switzerland University Children’s Hospital Zurich University of Zurich Switzerland Department of Radioncology and Radiation Theraphy Klinikum rechts der Isar Technical University of Munich Germany Department of Information Technology and Electrical Engineering ETH-Zurich Switzerland Department of Radiology Memorial Sloan Kettering Cancer Center New York City United States Department of Biomedical and Molecular Sciences Queen’s University Canada TranslaTUM - Central Institute for Translational Cancer Research Technical University of Munich Germany McGovern Institute Massachusetts Institute of Technology United States Institute for Diagnostic and Interventional Radiology Unveristy Zurich Hospital Switzerland BioMedIA Imperial College London United Kingdom Department of Radiation Oncology University of Pennsylvania PA United States University of Pennsylvania PA United States Department of Radiation Oncology Winship Cancer Institute of Emory University Georgia United States Nile University Cairo Egypt Department of Medical Sciences Acibadem University Istanbul Turkey Shri Guru Gobind Singhji Institute of Engineering and Technology Maharashtra Nanded India Trustworthy Machine Learning Lab University of Sydney Australia Image Sciences Institute University Medical Center Utrecht Netherlands Computer Engineering Department Istanbul Technical University Istanbul Turkey School of Computer Science Shenzhen University Shenzhen China University of Alberta United States University of Ljubljana Faculty of Electrical Engineering Ljubljana Slovenia Tongji University Shanghai China OPPO Research Institute Shanghai China School of Biological and Medical Engineering Beihang University Beijing China Harvard Medical School Boston

Uncertainty in medical image segmentation tasks, especially inter-rater variability, arising from differences in interpretations and annotations by various experts, presents a significant challenge in achieving consistent and reliable image segmentation. This variability not only reflects the inherent complexity and subjective nature of medical image interpretation but also directly impacts the development and evaluation of automated segmentation algorithms. Accurately modeling and quantifying this variability is essential for enhancing the robustness and clinical applicability of these algorithms. We report the set-up and summarize the benchmark results of the Quantification of Uncertainties in Biomedical image Quantification Challenge (QUBIQ), which was organized in conjunction with International Conferences on Medical image Computing and Computer-Assisted Intervention (MICCAI) 2020 and 2021. The challenge focuses on the uncertainty quantification of medical image segmentation which considers the omnipresence of inter-rater variability in imaging datasets. The large collection of images with multi-rater annotations features various modalities such as MRI and CT;various organs such as the brain, prostate, kidney, and pancreas;and different image dimensions 2D-vs-3D. A total of 24 teams submitted different solutions to the problem, combining various baseline models, Bayesian neural networks, and ensemble model techniques. The obtained results indicate the importance of the ensemble models, as well as the need for further research to develop efficient 3D methods for uncertainty quantification methods in 3D segmentation tasks. © 2024, CC BY-NC-ND.

关键词： image segmentation

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Tips and Tricks for Webly-Supervised Fine-Grained Recognition: Learning from the WebFG 2020 Challenge

arXiv

引用

arXiv 2020年

作者： Wei, Xiu-Shen Xu, Yu-Yan Yao, Yazhou Wei, Jia Xi, Si Xu, Wenyuan Zhang, Weidong Lv, Xiaoxin Fu, Dengpan Li, Qing Chen, Baoying Guo, Haojie Xue, Taolue Jing, Haipeng Wang, Zhiheng Zhang, Tianming Zhang, Mingwen PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology China Jiangsu Key Lab of Image Video Understanding for Social Security China Netease Games AI Lab Netease Yidun AI Lab

WebFG 2020 is an international challenge hosted by Nanjing University of science and technology, University of Edinburgh, Nanjing University, The University of Adelaide, Waseda University, etc. This challenge mainly pays attention to the webly-supervised fine-grained recognition problem. In the literature, existing deep learning methods highly rely on large-scale and high-quality labeled training data, which poses a limitation to their practicability and scalability in real world applications. In particular, for fine-grained recognition, a visual task that requires professional knowledge for labeling, the cost of acquiring labeled training data is quite high. It causes extreme difficulties to obtain a large amount of high-quality training data. Therefore, utilizing free web data to train fine-grained recognition models has attracted increasing attentions from researchers in the fine-grained community. This challenge expects participants to develop webly-supervised fine-grained recognition methods, which leverages web images in training fine-grained recognition models to ease the extreme dependence of deep learning methods on large-scale manually labeled datasets and to enhance their practicability and scalability. In this technical report, we have pulled together the top WebFG 2020 solutions of total 54 competing teams, and discuss what methods worked best across the set of winning teams, and what surprisingly did not help. © 2020, CC BY.

关键词： Scalability

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Norm-Aware Embedding for Efficient Person Search

Norm-Aware Embedding for Efficient Person Search

引用

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Di Chen Shanshan Zhang Jian Yang Bernt Schiele PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Max Planck Institute for Informatics Saarland Informatics Campus Jiangsu Key Lab of Image and Video Understanding for Social Security

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Person Search is a practically relevant task that aims to jointly solve Person Detection and Person Re-identification (re-ID). Specifically, it requires to find and locate all instances with the same identity as the query person in a set of panoramic gallery images. One major challenge comes from the contradictory goals of the two sub-tasks, i.e., person detection focuses on finding the commonness of all persons while person re-ID handles the differences among multiple identities. Therefore, it is crucial to reconcile the relationship between the two sub-tasks in a joint person search model. To this end, We present a novel approach called Norm-Aware Embedding to disentangle the person embedding into norm and angle for detection and re-ID respectively, allowing for both effective and efficient multi-task training. We further extend the proposal-level person embedding to pixel-level, whose discrimination ability is less affected by mis-alignment. We outperform other one-step methods by a large margin and achieve comparable performance to two-step methods on both CUHK-SYSU and PRW. Also, Our method is easy to train and resource-friendly, running at 12 fps on a single GPU.

关键词： Feature extraction Task analysis Training Detectors Standards Proposals Search problems

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SoccerNet 2023 Challenges Results

arXiv

引用

arXiv 2023年

作者： Cioppa, Anthony Giancola, Silvio Somers, Vladimir Magera, Floriane Zhou, Xin Mkhallati, Hassan Deliège, Adrien Held, Jan Hinojosa, Carlos Mansourian, Amir M. Miralles, Pierre Barnich, Olivier De Vleeschouwer, Christophe Alahi, Alexandre Ghanem, Bernard Van Droogenbroeck, Marc Kamal, Abdullah Maglo, Adrien Clapés, Albert Abdelaziz, Amr Xarles, Artur Orcesi, Astrid Scott, Atom Liu, Bin Lim, Byoungkwon Chen, Chen Deuser, Fabian Yan, Feng Yu, Fufu Shitrit, Gal Wang, Guanshuo Choi, Gyusik Kim, Hankyul Guo, Hao Fahrudin, Hasby Koguchi, Hidenari Ardö, Håkan Salah, Ibrahim Yerushalmy, Ido Muhammad, Iftikar Uchida, Ikuma Be'ery, Ishay Rabarisoa, Jaonary Lee, Jeongae Fu, Jiajun Yin, Jianqin Xu, Jinghang Nang, Jongho Denize, Julien Li, Junjie Zhang, Junpei Kim, Juntae Synowiec, Kamil Kobayashi, Kenji Zhang, Kexin Habel, Konrad Nakajima, Kota Jiao, Licheng Ma, Lin Wang, Lizhi Wang, Luping Li, Menglong Zhou, Mengying Nasr, Mohamed Abdelwahed, Mohamed Liashuha, Mykola Falaleev, Nikolay Oswald, Norbert Jia, Qiong Pham, Quoc-Cuong Song, Ran Hérault, Romain Peng, Rui Chen, Ruilong Liu, Ruixuan Baikulov, Ruslan Fukushima, Ryuto Escalera, Sergio Lee, Seungcheon Chen, Shimin Ding, Shouhong Someya, Taiga Moeslund, Thomas B. Li, Tianjiao Shen, Wei Zhang, Wei Li, Wei Dai, Wei Luo, Weixin Zhao, Wending Zhang, Wenjie Yang, Xinquan Ma, Yanbiao Joo, Yeeun Zeng, Yingsen Gan, Yiyang Zhu, Yongqiang Zhong, Yujie Ruan, Zheng Li, Zhiheng Huang, Zhijian Meng, Ziyu Belgium Saudi Arabia Sportradar Norway UCLouvain Belgium EPFL Switzerland EVS Broadcast Equipment Belgium Baidu Research United States Belgium Sharif University of Technology Iran Footovision France Zewail City of Science Technology and Innovation Egypt Université Paris-Saclay CEA France Universitat de Barcelona Spain Computer Vision Center Spain Nagoya University Japan Research Center for Applied Mathematics and Machine Intelligence Zhejiang Lab China AIBrain United States OPPO Research Institute China Germany Meituan China Tencent Youtu Lab China Amazon Prime Video Sport United States Sogang University Korea Republic of The University of Tokyo Japan Spiideo Sweden University of Tsukuba Japan School of Artificial Intelligence Beijing University of Posts and Telecommunications China Normandie Univ INSA Rouen LITIS France Shanghai Jiao Tong University China Key Laboratory of Intelligent Perception and Image Understanding The Ministry of Education Xidian University China NASK - National Research Institute Poland Robo Space China Tongji University China Sportlight Technology United Kingdom School of Control Science and Engineering Shandong University China lRomul Russia Aalborg University Denmark Turing AI Cultures GmbH Germany Information Systems Technology and Design Singapore University of Technology and Design Singapore Sun Yat-sen University China

The SoccerNet 2023 challenges were the third annual video understanding challenges organized by the SoccerNet team. For this third edition, the challenges were composed of seven vision-based tasks split into three main themes. The first theme, broadcast video understanding, is composed of three high-level tasks related to describing events occurring in the video broadcasts: (1) action spotting, focusing on retrieving all timestamps related to global actions in soccer, (2) ball action spotting, focusing on retrieving all timestamps related to the soccer ball change of state, and (3) dense video captioning, focusing on describing the broadcast with natural language and anchored timestamps. The second theme, field understanding, relates to the single task of (4) camera calibration, focusing on retrieving the intrinsic and extrinsic camera parameters from images. The third and last theme, player understanding, is composed of three low-level tasks related to extracting information about the players: (5) re-identification, focusing on retrieving the same players across multiple views, (6) multiple object tracking, focusing on tracking players and the ball through unedited video streams, and (7) jersey number recognition, focusing on recognizing the jersey number of players from tracklets. Compared to the previous editions of the SoccerNet challenges, tasks (2-3-7) are novel, including new annotations and data, task (4) was enhanced with more data and annotations, and task (6) now focuses on end-to-end approaches. More information on the tasks, challenges, and leader-boards are available on https://***. Baselines and development kits can be found on https://***/SoccerNet. © 2023, CC BY.

关键词： Computer vision

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Do not disturb me: Person re-identification under the interference of other pedestrians

arXiv

引用

arXiv 2020年

作者： Zhao, Shizhen Gao, Changxin Zhang, Jun Cheng, Hao Han, Chuchu Jiang, Xinyang Guo, Xiaowei Zheng, Wei-Shi Sang, Nong Sun, Xing Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Tencent Youtu Lab Sun Yat-sen University China

In the conventional person Re-ID setting, it is widely assumed that cropped person images are for each individual. However, in a crowded scene, off-shelf-detectors may generate bounding boxes involving multiple people, where the large proportion of background pedestrians or human occlusion exists. The representation extracted from such cropped images, which contain both the target and the interference pedestrians, might include distractive information. This will lead to wrong retrieval results. To address this problem, this paper presents a novel deep network termed Pedestrian-Interference Suppression Network (PISNet). PISNet leverages a Query-Guided Attention Block (QGAB) to enhance the feature of the target in the gallery, under the guidance of the query. Furthermore, the involving Guidance Reversed Attention Module and the Multi-Person Separation Loss promote QGAB to suppress the interference of other pedestrians. Our method is evaluated on two new pedestrian-interference datasets and the results show that the proposed method performs favorably against existing Re-ID methods. Our project is available at https://***/X-Brainlab/PI-ReID. Copyright © 2020, The Authors. All rights reserved.

关键词： Machine learning

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Efficient and Accurate Quantized image Super-Resolution on Mobile NPUs, Mobile AI & AIM 2022 challenge: Report

arXiv

引用

arXiv 2022年

作者： Ignatov, Andrey Timofte, Radu Denna, Maurizio Younes, Abdel Gankhuyag, Ganzorig Huh, Jingang Kim, Myeong Kyun Yoon, Kihwan Moon, Hyeon-Cheol Lee, Seungho Choe, Yoonsik Jeong, Jinwoo Kim, Sungjei Smyl, MacIej Latkowski, Tomasz Kubik, Pawel Sokolski, Michal Ma, Yujie Chao, Jiahao Zhou, Zhou Gao, Hongfan Yang, Zhengfeng Zeng, Zhenbing Zhuge, Zhengyang Li, Chenghua Zhu, Dan Sun, Mengdi Duan, Ran Gao, Yan Kong, Lingshun Sun, Long Li, Xiang Zhang, Xingdong Zhang, Jiawei Wu, Yaqi Pan, Jinshan Yu, Gaocheng Zhang, Jin Zhang, Feng Ma, Zhe Wang, Hongbin Cho, Hojin Kim, Steve Li, Huaen Ma, Yanbo Luo, Ziwei Li, Youwei Yu, Lei Wen, Zhihong Wu, Qi Fan, Haoqiang Liu, Shuaicheng Zhang, Lize Zong, Zhikai Kwon, Jeremy Zhang, Junxi Li, Mengyuan Fu, Nianxiang Ding, Guanchen Zhu, Han Chen, Zhenzhong Li, Gen Zhang, Yuanfan Sun, Lei Zhang, Dafeng Yang, Neo Liu, Fitz Zhao, Jerry Ayazoglu, Mustafa Bilecen, Bahri Batuhan Hirose, Shota Arunruangsirilert, Kasidis Ao, Luo Leung, Ho Chun Wei, Andrew Liu, Jie Liu, Qiang Yu, Dahai Li, Ao Luo, Lei Zhu, Ce Hong, Seongmin Park, Dongwon Lee, Joonhee Lee, Byeong Hyun Lee, Seunggyu Chun, Se Young He, Ruiyuan Jiang, Xuhao Ruan, Haihang Zhang, Xinjian Liu, Jing Gendy, Garas Sabor, Nabil Hou, Jingchao He, Guanghui Computer Vision Lab Eth Zürich Switzerland Ai Witchlabs Switzerland University of Würzburg Germany Synaptics Europe Switzerland Korea Republic of Yonsei University Korea Republic of Tcl Research Europe Poland East China Normal University China Institute of Automation Chinese Academy of Sciences China Boe Technology Group Co. Ltd. China Nanjing University of Science and Technology China Ant Group China GenGenAI Korea Republic of Hefei University of Technology China Megvii Technology China China Xidian University China Qingdao Hi-image Technologies Co. Ltd. China Korea Republic of Wuhan University China China Samsung Research China Aselsan Corporation Turkey Waseda University Japan Tcl Corporate Research China University of Electronic Science and Technology China Intelligent Computational Imaging Lab Seoul National University Korea Republic of Bilibili Inc. China Shanghai Jiao Tong University China Assiut University Egypt

image super-resolution is a common task on mobile and IoT devices, where one often needs to upscale and enhance low-resolution images and video frames. While numerous solutions have been proposed for this problem in the past, they are usually not compatible with low-power mobile NPUs having many computational and memory constraints. In this Mobile AI challenge, we address this problem and propose the participants to design an efficient quantized image super-resolution solution that can demonstrate a real-time performance on mobile NPUs. The participants were provided with the DIV2K dataset and trained INT8 models to do a high-quality 3X image upscaling. The runtime of all models was evaluated on the Synaptics VS680 Smart Home board with a dedicated edge NPU capable of accelerating quantized neural networks. All proposed solutions are fully compatible with the above NPU, demonstrating an up to 60 FPS rate when reconstructing Full HD resolution images. A detailed description of all models developed in the challenge is provided in this paper. Copyright © 2022, The Authors. All rights reserved.

关键词： image enhancement

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Analyzing Diversity in Healthcare LLM Research: A Scientometric Perspective

arXiv

引用

arXiv 2024年

作者： Restrepo, David Wu, Chenwei Vásquez-Venegas, Constanza Matos, João Gallifant, Jack Celi, Leo Anthony Bitterman, Danielle S. Nakayama, Luis Filipe Laboratory for Computational Physiology Massachusetts Institute of Technology CambridgeMA United States Departamento de Telematica Universidad del Cauca Cauca Popayán Colombia Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI United States Scientific Image Analysis Lab Faculty of Medicine Universidad de Chile RM Santiago Chile Department of Computer Science Faculty of Engineering Universidad de Concepción Biobio Concepción Chile Department of Biostatistics Harvard TH Chan School of Public Health BostonMA United States Department of Medicine Beth Israel Deaconess Medical Center BostonMA United States Artificial Intelligence in Medicine Program Mass General Brigham Harvard Medical School BostonMA United States Department of Ophthalmology São Paulo Federal University São Paulo São Paulo Brazil

The deployment of large language models (LLMs) in healthcare has demonstrated substantial potential for enhancing clinical decision-making, administrative efficiency, and patient outcomes. However, the underrepresentation of diverse groups in the development and application of these models can perpetuate biases, leading to inequitable healthcare delivery. This paper presents a comprehensive scientometric analysis of LLM research for healthcare, including data from January 1, 2021, to July 1, 2024. By analyzing metadata from PubMed and Dimensions, including author affiliations, countries, and funding sources, we assess the diversity of contributors to LLM research. Our findings highlight significant gender and geographic disparities, with a predominance of male authors and contributions primarily from high-income countries (HICs). We introduce a novel journal diversity index based on Gini diversity to measure the inclusiveness of scientific publications. Our results underscore the necessity for greater representation in order to ensure the equitable application of LLMs in healthcare. We propose actionable strategies to enhance diversity and inclusivity in artificial intelligence research, with the ultimate goal of fostering a more inclusive and equitable future in healthcare innovation. © 2024, CC BY.

关键词： Decision making

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Characterization Of Spatial Dynamics Of Fmri Data In White Matter Using Diffusion-Informed White Matter Harmonics

Characterization Of Spatial Dynamics Of Fmri Data In White M...

引用

IEEE International Symposium on Biomedical Imaging

作者： Hamid Behjat Iman Aganj David Abramian Anders Eklund Carl-Fredrik Westin Brigham and Women’s Hospital Harvard Medical School Boston USA Martinos Center for Biomedical Imaging Massachusetts General Hospital Harvard Medical School Boston USA Lund University Lund Sweden Computer Science and Artificial Intelligence Lab Massachusetts Institute of Technology Cambridge USA Linköping University Linköping Sweden Center for Medical Image Science and Visualization Linköping University Linköping Sweden

In this work, we leverage the Laplacian eigen basis of voxel-wise white matter (WM) graphs derived from diffusion weighted MRI data, dubbed WM harmonics, to characterize the spatial structure of WM fMRI data. Our motivation for such a characterization is based on studies that show WM fMRI data exhibit a spatial correlational anisotropy that coincides with underlying fiber patterns. By quantifying the energy content of WM fMRI data associated with subsets of WM harmonics across multiple spectral bands, we show that the data exhibits notable subtle spatial modulations under functional load that are not manifested during rest. WM harmonics provide a novel means to study the spatial dynamics of WM fMRI data, in such way that the analysis is informed by the underlying anatomical structure.

关键词： Laplace equations Anisotropic magnetoresistance Modulation Anatomical structure Functional magnetic resonance imaging Harmonic analysis Spatial databases

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Synth-by-Reg (SbR): Contrastive learning for synthesis-based registration of paired images

arXiv

引用

arXiv 2021年

作者： Casamitjana, Adrià Mancini, Matteo Iglesias, Juan Eugenio Center for Medical Image Computing University College London United Kingdom Department of Neuroscience University of Sussex Brighton United Kingdom NeuroPoly Lab Polytechnique Montreal Canada CUBRIC Cardiff University United Kingdom Martinos Center for Biomedical Imaging MGH Harvard Medical School United States Computer Science and AI Laboratory Massachusetts Institute of Technology United States

Nonlinear inter-modality registration is often challenging due to the lack of objective functions that are good proxies for alignment. Here we propose a synthesis-by-registration method to convert this problem into an easier intra-modality task. We introduce a registration loss for weakly supervised image translation between domains that does not require perfectly aligned training data. This loss capitalises on a registration U-Net with frozen weights, to drive a synthesis CNN towards the desired translation. We complement this loss with a structure preserving constraint based on contrastive learning, which prevents blurring and content shifts due to overfitting. We apply this method to the registration of histological sections to MRI slices, a key step in 3D histology reconstruction. Results on two public datasets show improvements over registration based on mutual information (13% reduction in landmark error) and synthesis-based algorithms such as CycleGAN (11% reduction), and are comparable to registration with label supervision. Code and data are publicly available at https://***/acasamitjana/SynthByReg. © 2021, CC BY.

关键词： Digital storage

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Neighborhood linear discriminant analysis

引用

Pattern Recognition 2022年 123卷

作者： Fa Zhu Junbin Gao Jian Yang Ning Ye College of Information Science and Technology Nanjing Forestry University Nanjing 210037 PR China Discipline of Business Analytics University of Sydney Business School University of Sydney NSW 2006 Australia PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education and the Jiangsu Key Lab of Image and Video Understanding for Social Security the School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing 210094 PR China

Linear Discriminant Analysis (LDA) assumes that all samples from the same class are independently and identically distributed (i.i.d.). LDA may fail in the cases where the assumption does not hold. Particularly when a class contains several clusters (or subclasses), LDA cannot correctly depict the internal structure as the scatter matrices that LDA relies on are defined at the class level. In order to mitigate the problem, this paper proposes a neighborhood linear discriminant analysis (nLDA) in which the scatter matrices are defined on a neighborhood consisting of reverse nearest neighbors. Thus, the new discriminator does not need an i.i.d. assumption. In addition, the neighborhood can be naturally regarded as the smallest subclass, for which it is easier to be obtained than subclass without resorting to any clustering algorithms. The projected directions are sought to make sure that the within-neighborhood scatter as small as possible and the between-neighborhood scatter as large as possible, simultaneously. The experimental results show that nLDA performs significantly better than previous discriminators, such as LDA, LFDA, ccLDA, LM-NNDA, and l 2 , 1 -RLDA.

关键词： Linear discriminant analysis Reverse nearest neighbors Neighborhood linear discriminant analysis Multimodal class

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