2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML)最新文献_第4页

Off Line Handwritten Signature Verification Based on Feature Fusion 基于特征融合的离线手写签名验证

2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML) Pub Date : 2021-07-16 DOI: 10.1109/PRML52754.2021.9520737

Nurbiya Xamxidin, Mahpirat Mamat, Wenxiong Kang, A. Aysa, K. Ubul

引用次数: 0

Research on Human Action Recognition Based on Global-Local Features of Video 基于视频全局-局部特征的人体动作识别研究

2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML) Pub Date : 2021-07-16 DOI: 10.1109/PRML52754.2021.9520743

Miao Jin, Jun Zhang, Tianfu Huang, Zhiwei Guo, Xiwen Chen

引用次数: 1

Chinese License Plate Recognition Using Machine and Deep Learning Models 基于机器和深度学习模型的中国车牌识别

2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML) Pub Date : 2021-07-16 DOI: 10.1109/PRML52754.2021.9520386

Xiaoyu Zhang, Xu Ni, Y. Deng, Changyu Jiang, Mina Maleki

引用次数: 2

Impact of Visual Noise in Activity Recognition Using Deep Neural Networks - An Experimental Approach 视觉噪声在深度神经网络活动识别中的影响-一种实验方法

2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML) Pub Date : 2021-07-16 DOI: 10.1109/PRML52754.2021.9520734

Leonardo Capozzi, P. Carvalho, Afonso Sousa, C. Pinto, João Ribeiro Pinto, Jaime S. Cardoso

引用次数: 0

Depth of Anesthesia Monitoring Method Based on EEG Microstate Analysis and Hidden Markov Model 基于脑电微态分析和隐马尔可夫模型的麻醉深度监测方法

2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML) Pub Date : 2021-07-16 DOI: 10.1109/PRML52754.2021.9520709

Lichengxi Si, Zhian Liu, G. Wang

{"title":"Depth of Anesthesia Monitoring Method Based on EEG Microstate Analysis and Hidden Markov Model","authors":"Lichengxi Si, Zhian Liu, G. Wang","doi":"10.1109/PRML52754.2021.9520709","DOIUrl":"https://doi.org/10.1109/PRML52754.2021.9520709","url":null,"abstract":"Electroencephalogram (EEG) microstate analysis is an important emerging method that can classify continuous multichannel EEG signals into a limited number of microstates through clustering. Microstate analysis combines the time and space information of EEG, which can reflect important transformation process of high-level cognitive functions in the brain. In recent years, Microstate analysis has made great progress in the research of depth of anesthesia (DOA) monitoring. In this paper, a new DOA monitoring algorithm is designed by combining microstate sequence and hidden Markov model (HMM). The trained Hidden Markov Model shows the information of brain nerve activity hidden in the microstate sequence, which can effectively distinguish the mental states of different DOAs, thereby realizing the corresponding DOA classification. The experimental dataset was obtained from an open-access section of the University of Cambridge Data Repository, which contains EEG data from 20 healthy subjects. During propofol injection, the brain states of the subjects were divided into four conditions: baseline (BS), mild sedation (ML), moderate sedation (MD), and the recovery stage (RC). The algorithm classified BS and ML, BS and MD, ML and MD with the accuracy rates of 71.40%, 73.48%, 67.75% respectively. This shows that the microstate analysis has great application potential in the study of anesthesia. Hidden Markov model training for microstate sequences can become a new research direction for DOA monitoring.","PeriodicalId":429603,"journal":{"name":"2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126912894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Artificial Intelligence Use in Human Resources Management: Strategy and Operation’s Impact 人工智能在人力资源管理中的应用:战略和运营的影响

2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML) Pub Date : 2021-07-16 DOI: 10.1109/PRML52754.2021.9520719

S. Achchab, Yassine Khallouk Temsamani

引用次数: 4

Language Identification Research Based on Dual Attention Mechanism 基于双重注意机制的语言识别研究

2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML) Pub Date : 2021-07-16 DOI: 10.1109/PRML52754.2021.9520699

Mijit Ablimit, Ma Xueli, A. Hamdulla

{"title":"Language Identification Research Based on Dual Attention Mechanism","authors":"Mijit Ablimit, Ma Xueli, A. Hamdulla","doi":"10.1109/PRML52754.2021.9520699","DOIUrl":"https://doi.org/10.1109/PRML52754.2021.9520699","url":null,"abstract":"Language identification(LID) is an important branch of speech technology. A key problem of language identification is how to extract effective speech segment representation from a given speech and improve the model performance. In recent years, deep learning has made significant progress in the application of language identification. Neural networks can be used to extract relevant features and effectively improve system performance. In order to solve the problem of poor feature extraction ability and low recognition rate, this paper considers both features and models, through the comparison of features such as MFCC, Fbank to determine spectrogram as the best input feature, and proposes a language identification method based on dual attention mechanism. This method first takes the spectrogram of the speech spectrogram, and converts it into a gray-scale spectrogram as input, uses a multi-level convolutional neural network to capture local features, extracts dual attention in channel and spatial dimension of the feature map through the CBAM module, catches temporal characteristics with bidirectional gated recurrent units, then transfers the local characteristics and timing characteristics jointly to a fully connected layer, and uses the fully connected layer to output language classes. This paper conducts experiments on the Common voice dataset and AP17-OLR dataset, it demonstrates that dual attention mechanism’s language identification method can achieve good results, increase the feature extraction ability and improve the performance of language identification.","PeriodicalId":429603,"journal":{"name":"2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134016827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Robust Real-Time Human Action Detection through the Fusion of 3D and 2D CNN 基于三维和二维CNN融合的鲁棒实时人体动作检测

2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML) Pub Date : 2021-07-16 DOI: 10.1109/PRML52754.2021.9520696

Edwin Kwadwo Tenagyei, Zongbo Hao, Kwadwo Kusi, K. Sarpong

{"title":"Robust Real-Time Human Action Detection through the Fusion of 3D and 2D CNN","authors":"Edwin Kwadwo Tenagyei, Zongbo Hao, Kwadwo Kusi, K. Sarpong","doi":"10.1109/PRML52754.2021.9520696","DOIUrl":"https://doi.org/10.1109/PRML52754.2021.9520696","url":null,"abstract":"Recent approaches for human action detection often rely on appearance and optical flow networks for frame-level detections before linking them to form action tubes. However, they achieve unsatisfactory performance in real-time due to their huge computational complexity and large parameter usage during training. In this paper, we design and implement a unified end-to-end convolutional neural network (CNN) architecture that consists of two branches, extracting both spatial and temporal information concurrently before predicting bounding boxes and action probabilities from video clips. We also design a novel mechanism that exploits the inter-channel dependencies for an effective fusion of features from the branches. Specifically, we propose a Channel Fusion and Relation-Global Attention (CFRGA) module to aggregate the two features smoothly and model their inter-channel dependencies by considering their global scope structural relation information when inferring attention. We conduct experiments on the untrimmed video dataset, UCF101-24, and achieved impressive results in frame-mAP and video-mAP. The experimental results show that our channel fusion and relation-global attention module contributes to its good performance.","PeriodicalId":429603,"journal":{"name":"2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134499337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Novel Methodology for Automating Spatio-Temporal Data Classification in Basketball Using Active Learning 基于主动学习的篮球运动时空数据自动分类新方法

2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML) Pub Date : 2021-07-16 DOI: 10.1109/PRML52754.2021.9520715

Shaojun Ai, Jiaming Na, V. D. Silva, M. Caine

{"title":"A Novel Methodology for Automating Spatio-Temporal Data Classification in Basketball Using Active Learning","authors":"Shaojun Ai, Jiaming Na, V. D. Silva, M. Caine","doi":"10.1109/PRML52754.2021.9520715","DOIUrl":"https://doi.org/10.1109/PRML52754.2021.9520715","url":null,"abstract":"The use of machine learning on spatio-temporal datasets has generated significant interest in a range of applications, including vehicular traffic modelling and urban planning. One of the most prolific application domains is sports analytics due to the availability of real-world multi-agent datasets, where such techniques are used to recognize and predict offensive and defensive strategies in a range of team sports. However, the use of advanced machine learning techniques requires the large datasets to be annotated by domain experts, which is a time-consuming task. Active learning is a methodology that significantly cuts down the data-annotation time on large datasets. In this paper, we investigate active learning strategies to annotate spatio-temporal datasets for the purpose of classification model building. The proposed algorithms are demonstrated on a dataset obtained from professional basketball games to classify an offensive strategy known as ‘Pick-and-Roll’. Several neural network architectures are investigated for the classification of more than 900 segments of basketball plays. The results obtained suggest that the proposed, preferred, methodology is well suited for annotating large spatio-temporal datasets and has the potential to be applicable across a range of team sports and non-sports usage scenarios.","PeriodicalId":429603,"journal":{"name":"2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130806372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

SFTRLS-Based Speech Enhancement Method Using CNN to Determine the Noise Type and the Optimal Forgetting Factor 基于sftrls的语音增强方法，利用CNN确定噪声类型和最佳遗忘因子

2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML) Pub Date : 2021-07-16 DOI: 10.1109/PRML52754.2021.9520741

De-You Tang, Guoqiang Chen

引用次数: 0