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2021 10th International Conference on Computing and pattern recognition

作者： Zimin Tian Si Chen Da-Han Wang Junwen Lu School of Computer and Information Engineering Xiamen University of Technology China and Fujian Key Laboratory of Pattern Recognition and Image Understanding China School of Computer and Information Engineering Xiamen University of Technology China

ISBN: (纸本)9781450390439

Person re-identification (Re-ID) has been a popular research topic in computer vision in recent years, and it has important application value in numerous fields, such as intelligent security. The person Re-ID task is to identify whether the pedestrians appearing under different cameras are the same person. The traditional person Re-ID methods mainly rely on the characteristics of manual design, and it has difficulty in solving the problems of person occlusion, posture change, and illumination variation. With the wide application of deep learning, the person Re-ID based on deep learning has brought new ideas for solving these problems, and has been widely concerned by scholars. This paper summarizes and analyzes the latest research trends of person Re-ID based on deep learning. In our work, the recent research works of person Re-ID are coarsely categorized into the supervised learning methods and the unsupervised learning methods according to whether the pedestrian images in the training set have real labels. We then describe the representative datasets used in the person Re-ID task. Finally, we conclude and discuss the future directions of the person Re-ID based on deep learning.

关键词： Person re-identification Supervised learning Unsupervised learning Deep learning

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Handwritten Mathematical Expression recognition with Self-Attention

Handwritten Mathematical Expression Recognition with Self-At...

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2021年第四届算法、计算和人工智能国际会议

作者： Xueke Chi Da-Han Wang Yuefeng Wu Yun Wu Fujian Key Laboratory of Pattern Recognition and Image Understanding School of Computer and Information Engineering Xiamen University of Technology

Attention-based encoder-decoder models have made great success on handwritten mathematical expression recognition in recent years. However, this kind of method has the problem of attention drift, because under the local attention mechanism based on RNN,the high similarity between coding features can cause attention confusion. To settle this problem, we propose an encoder-decoder model with self-attention, which captures the global information of the feature map and fuses the local information of the CNN as complementary features. Experiments are conducted on the CROHME2014 and CROHME 2016 competition datasets. The experimental results show that, when only using the official training dataset, the proposed method achieves recognition accuracies of51.98% and 50.74% on the CROHME2014 and CROHME2016 competition datasets, respectively, which outperforms the other methods significantly. The improvements demonstrate the effectiveness of the self-attention module.

关键词： offline recognition handwritten mathematical expression selfattention non-local

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Local Neighbor Propagation on Graphs for Robust Feature Matching

SSRN

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SSRN 2023年

作者： Guo, Hanlin Xiao, Guobao Su, Lumei Zhou, Jiaxing Wang, Dahan Xiamen Key Laboratory of Frontier Electric Power Equipment and Intelligent Control School of Electrical Engineering and Automation Xiamen University of Technology China Fujian Key Laboratory of Sensing and Computing for Smart Cities School of Information Science and Engineering Xiamen University China College of Computer and Control Engineering Minjiang University China Fujian Key Laboratory of Pattern Recognition and Image Understanding School of Computer and Information Engineering Xiamen University of Technology China

Establishing reliable correspondences between two sets of feature points is a critical preprocessing step in many computer vision and pattern recognition tasks. In this paper, we propose a novel robust Local Neighbor Propagation on Graphs based feature matching (LNPG) method, to obtain good correspondences for feature matching. LNPG starts from a novel neighborhood graph construction strategy. The strategy leverages the spatial consistency constraint to generate a series of neighborhood sets, and employs the residual information to preserve the local neighborhood relationships of potential inliers (i.e., true matches). Subsequently, LNPG incorporates local neighbor propagation into the graph to enhance connections between data in different neighborhoods, by using the path-based similarity measurement and the adaptive graph partition. In addition, LNPG includes a novel consistency-filtering-based clustering algorithm for robust feature matching. This clustering algorithm introduces a reliable neighborhood consistency measure function and an effective cluster merging criterion for cluster detection and cluster merging during the clustering process, respectively. Overall, LNPG not only effectively distinguishes inliers from outliers, but also reliably classifies inliers into different transformation models between pairs of images. Experiments on publicly available datasets with different types of image transformations show the superiority of our LNPG in comparison with other state-of-the-art methods. © 2023, The Authors. All rights reserved.

关键词： Clustering algorithms

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A comprehensive survey of oracle character recognition: challenges, benchmarks, and beyond

arXiv

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arXiv 2024年

作者： Li, Jing Chi, Xueke Wang, Qiufeng Huang, Kaizhu Wang, Da-Han Liu, Yongge Liu, Cheng-Lin College of Science & Technology Ningbo University Ningbo China School of Advanced Technology Xi’an Jiaotong-Liverpool University Suzhou China Duke Kunshan University Suzhou China Fujian Key Laboratory of Pattern Recognition and Image Understanding School of Computer and Information Engineering Xiamen University of Technology Xiamen China Anyang Normal University Anyang China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China

Oracle character recognition—an analysis of ancient Chinese inscriptions found on oracle bones—has become a pivotal field intersecting archaeology, paleography, and historical cultural studies. Traditional methods of oracle character recognition have relied heavily on manual interpretation by experts, which is not only labor-intensive but also limits broader accessibility to the general public. With recent breakthroughs in pattern recognition and deep learning, there is a growing movement towards the automation of oracle character recognition (OrCR), showing considerable promise in tackling the challenges inherent to these ancient scripts. However, a comprehensive understanding of OrCR still remains elusive. Therefore, this paper presents a systematic and structured survey of the current landscape of OrCR research. We commence by identifying and analyzing the key challenges of OrCR. Then, we provide an overview of the primary benchmark datasets and digital resources available for OrCR. A review of contemporary research methodologies follows, in which their respective efficacies, limitations, and applicability to the complex nature of oracle characters are critically highlighted and examined. Additionally, our review extends to ancillary tasks associated with OrCR across diverse disciplines, providing a broad-spectrum analysis of its applications. We conclude with a forward-looking perspective, proposing potential avenues for future investigations that could yield significant advancements in the field. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

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AMST: Object tracking based on collaborative framework with adaptive multi-strategy

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Information Sciences 2025年 718卷

作者： Rui Xu Si Chen Yan Yan Da-Han Wang Shunzhi Zhu Fujian Key Laboratory of Pattern Recognition and Image Understanding School of Computer and Information Engineering Xiamen University of Technology Xiamen 361024 China Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics Xiamen University Xiamen 361005 China

Deep object tracking can be modeled by online learning to adapt to the appearance changes, or offline learning to achieve fast tracking speed. However, either online or offline learning trackers are still difficult to continuously cope with challenging tracking scenes, due to the cumulative errors easily caused by online learning trackers and the object appearance changes often neglected by offline learning trackers. To overcome this problem, we propose a novel object tracking method based on collaborative framework with adaptive multi-strategy, enabling automatic switch between the online and offline learning trackers. In this framework, we first design the variable action set (VAS) module to adaptively choose the different trackers, tracking strategies, and template strategies with multiple action labels. Moreover, we incorporate the template update and selection (TUS) module, which dynamically updates and selects templates from a memory unit to adapt to object appearance changes. To update the action set and the memory unit, we further devise the online reliability evaluation (ORE) module that not only evaluates the tracking result and the tracker itself, but also estimates the quality and quantity of templates. Comprehensive experiments on challenging short-term and long-term tracking benchmarks demonstrate the remarkable performance of the proposed method.

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Journal of Network Intelligence

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Journal of Network Intelligence 2022年第4期7卷 848-861页

作者： Weng, Wei Li, Tong Liao, Jian-Chao Guo, Feng Chen, Fen Wei, Bo-Wen College of Computer and Information Engineering Xiamen University of Technology Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361024 China College of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Fujian Newland Auto-ID Tech. Co. Ltd Fuzhou350015 China Xiamen Fuyun Information Tech. Co. Ltd Xiamen361008 China

Graph clustering is a fundamental method for studying complex networks. Some existing approaches focus on the graph data without attributed information. However, graph data in the real world generally have attribute information, which can better describe complex networks. Recently, deep learning-based methods are widely used for attributed graph clustering. DAEGC is a goal-directed method, which utilizes graph attention encoders for latent information learning and performs competitively in attributed graph clustering. To better aggregate the structure and node attribute information in DAEGC, we employ a lightweight attention mechanism to capture the relationship between nodes and their neighbors, learn hidden representations, and use a self-training module to train the model for optimal results. Extensive experiments show that our proposed method improves DAEGC and achieves superior performance compared with state-of-the-art algorithms in attributed graph clustering. © 2022, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Complex networks

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An Optimizing Parameters and Feature Selection in SVM Based on Improved Cockroach Swarm Optimization 16th

An Optimizing Parameters and Feature Selection in SVM Based ...

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16th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2020 in conjunction with the 13th International Conference on Frontiers of Information Technology, Applications and Tools, FITAT 2020

作者： Nguyen, Trong-The Yu, Jie Nguyen, Thi-Thanh-Tan Lai, Quoc-Anh Ngo, Truong-Giang Dao, Thi-Kien Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China College of Mechanical and Automotive Engineering Fujian University of Technology Fuzhou China Information Technology Faculty Electric Power University Hanoi Viet Nam Department of Pattern Recognition & Image Processing Institute of Information Technology Vietnam Academy of Science and Technology Hanoi Viet Nam Faculty of Computer Science and Engineering Thuyloi University 175 Tay Son Dong Da Hanoi Viet Nam

ISBN: (纸本)9789813367562

This study improves a classifier of the support vector machine (SVM) by optimizing its parameters by adjusting cockroach swarm optimization (CSO). Classification system design includes data inputs, pre-process, and classification. The relief technique selects the feature subset, and the kernel parameters used as input for the system. The feature selection and classification of data sets were figured out by the combination of ICSO and SVM with its optimized parameters synchronous. Simulation results of the proposed scheme are compared with other systems, e.g., GA-SVM, PSO-SVM, and CSO-SVM, which show that the proposed project can achieve better classification accuracy than the different algorithms. It is promising that the proposed scheme can be applied to high-dimensional data classification, such as image classification, signal classification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： Support vector machines

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2D License Plate recognition based on Automatic Perspective Rectification

2D License Plate Recognition based on Automatic Perspective ...

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International Conference on pattern recognition

作者： Hui Xu Zhao-Hong Guo Da-Han Wang Xiang-Dong Zhou Yu Shi Chongqing school University of Chinese Academy of Sciences china Fujian Key Laboratory of Pattern Recognition and Image Understanding School of Computer and Information Engineering Xiamen University of Technology China Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences

License plate recognition (LPR) remains a challenging task in face of some difficulties such as image deformation and multi-line character distribution. Text rectification that is crucial to eliminate the effects of image deformation has attracted increasing attentions in scene text recognition. However, current text rectification methods are not designed specifically for LPR, which did not take the features of plate deformation into account. Considering the fact that a license plate (LP) can only generate perspective distortion in the image due to its rigid feature, in this paper we propose a novel perspective rectification network (PRN) to automatically estimate the perspective transformation and rectify the distorted LP accordingly. For recognition, we propose a location-aware 2D attention based recognition network that is capable of recognizing both single-line and double-line plates with perspective deformation. The rectification network and recognition network are connected for end-to-end training. Experiments on common datasets show that the proposed method achieves the state-of-the-art performance, demonstrating the effectiveness of the proposed approach.

关键词： Training Text recognition Face recognition Licenses Benchmark testing Distortion Optical character recognition software

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CAMEL2: Enhancing weakly supervised learning for histopathology images by incorporating the significance ratio

arXiv

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arXiv 2023年

作者： Xu, Gang Wang, Shuhao Zhao, Lingyu Chen, Xiao Wang, Tongwei Wang, Lang Luo, Zhenwei Wang, Dahan Zhang, Zewen Liu, Aijun Ba, Wei Song, Zhigang Shi, Huaiyin Zhong, Dingrong Ma, Jianpeng Multiscale Research Institute of Complex Systems Fudan University Shanghai China Zhangjiang Fudan International Innovation Center Fudan University Shanghai China Shanghai AI Laboratory Shanghai China Thorough Lab Thorough Future Beijing China Department of Pathology China-Japan Friendship Hospital Beijing China Minjiang University Fuzhou China College of Future Technology Peking University Beijing China Fujian Key Laboratory of Pattern Recognition and Image Understanding School of Computerand Information Engineering Xiamen University of Technology Xiamen China Department of Pathology Seventh Medical Center Chinese PLA General Hospital Beijing China Department of Pathology First Medical Center Chinese PLA General Hospital Beijing China

Histopathology image analysis plays a crucial role in cancer diagnosis. However, training a clinically applicable segmentation algorithm requires pathologists to engage in labour-intensive labelling. In contrast, weakly supervised learning methods, which only require coarse-grained labels at the image level, can significantly reduce the labeling efforts. Unfortunately, while these methods perform reasonably well in slide-level prediction, their ability to locate cancerous regions, which is essential for many clinical applications, remains unsatisfactory. Previously, we proposed CAMEL, which achieves comparable results to those of fully supervised baselines in pixel-level segmentation. However, CAMEL requires 1,280×1,280 image-level binary annotations for positive WSIs. Here, we present CAMEL2, by introducing a threshold of the cancerous ratio for positive bags, it allows us to better utilize the information, consequently enabling us to scale up the image-level setting from 1,280×1,280 to 5,120×5,120 while maintaining the accuracy. Our results with various datasets, demonstrate that CAMEL2, with the help of 5,120×5,120 image-level binary annotations, which are easy to annotate, achieves comparable performance to that of a fully supervised baseline in both instance- and slide-level classifications. © 2023, CC BY.

关键词： image enhancement

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A comprehensive survey of oracle character recognition: Challenges, datasets, methodology, and beyond

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pattern recognition 2025年

作者： Jing Li Xueke Chi Qiufeng Wang Kaizhu Huang Da-Han Wang Yongge Liu Cheng-Lin Liu College of Science & Technology Ningbo University Ningbo China School of Advanced Technology Xi’an Jiaotong-Liverpool University Suzhou China Duke Kunshan University Suzhou China Fujian Key Laboratory of Pattern Recognition and Image Understanding School of Computer and Information Engineering Xiamen University of Technology Xiamen China Anyang Normal University Anyang China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China

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