11th International Conference of Pattern Recognition Systems (ICPRS 2021)最新文献_第4页

Evaluating the performance of convolutional neural networks to detect deforested regions in the Brazilian Legal Amazon using LandSat-8 satellite images 使用LandSat-8卫星图像评估卷积神经网络在巴西合法亚马逊地区检测森林砍伐区域的性能

11th International Conference of Pattern Recognition Systems (ICPRS 2021) Pub Date : 1900-01-01 DOI: 10.1049/icp.2021.1430

F. C. Costa, M. Costa, C. C. Costa Filho

引用次数: 0

Attribute classification for the analysis of genuineness of facial expressions 基于属性分类的面部表情真实性分析

11th International Conference of Pattern Recognition Systems (ICPRS 2021) Pub Date : 1900-01-01 DOI: 10.1049/icp.2021.1467

G. Florio, M. Buemi, D. Acevedo, P. Negri

{"title":"Attribute classification for the analysis of genuineness of facial expressions","authors":"G. Florio, M. Buemi, D. Acevedo, P. Negri","doi":"10.1049/icp.2021.1467","DOIUrl":"https://doi.org/10.1049/icp.2021.1467","url":null,"abstract":"In this work we study different artificial neural network variants to classify instances of facial expressions on video according to its genuineness. This problem is a task not trivial to solve by human beings. The main analysis compares deep feed-forward neural networks with recurrent neural networks. This particular type of network capable of extracting information from a sequence and keep it through time. In that way, a video can be classified using not only its features but also the ones from its predecessors. Since the amount of videos in the dataset is rather scarce, a new metric is proposed to make a more particularized analysis. Results suggest that certain facial features that allows distinguishing a genuine expression and a faked one are too related to the subject that performs them, which suggests that developing an universal classifier (independent of the subject) seems unfeasible. Regarding the comparison between the two types of networks, although the recurrent variants cannot outperform convnets, we can observe that they achieve similar results but with a smaller amount of training epochs. The dataset used in this paper was originated for the Real Versus Fake Expressed Emotion Challenge at the ICCV 2017.","PeriodicalId":431144,"journal":{"name":"11th International Conference of Pattern Recognition Systems (ICPRS 2021)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125523746","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

Machine Learning techniques for Behavioral Feature Selection in Network Intrusion Detection Systems 网络入侵检测系统中行为特征选择的机器学习技术

11th International Conference of Pattern Recognition Systems (ICPRS 2021) Pub Date : 1900-01-01 DOI: 10.1049/icp.2021.1448

Vicente Martínez, Rodrigo Salas, Oliver Tessini, Romina Torres

{"title":"Machine Learning techniques for Behavioral Feature Selection in Network Intrusion Detection Systems","authors":"Vicente Martínez, Rodrigo Salas, Oliver Tessini, Romina Torres","doi":"10.1049/icp.2021.1448","DOIUrl":"https://doi.org/10.1049/icp.2021.1448","url":null,"abstract":"Information systems are prone to receiving multiple types of attacks over the network. Therefore, Network Intrusion Detection Systems (NIDSs) analyze the behavior of the network traffic to detect anomalies and eventual cyberattacks. The NIDS must be able to detect these cyberattacks in an efficient and effective manner based on a set of features where it is expected that the performance depends on both the selected features and the machine learning technique used. The main goal of this work is to identify the most relevant characteristics required to detect, with a high sensitivity and precision, between normal traffic and a network intrusion, together with the most relevant features associated to the identification of a specific type of attack. In this work, a comparative study of different decision tree-based machine learning techniques combined with several feature selection techniques in order to accomplish the goal. Random Forest and the XGBoost achieved a performance that reaches up to 98.5% in the F-measure when the complete set of features were used. Results show the performance was just slightly reduced to 98% when the 10 most relevant features were used. Moreover, results also show that the model using only the 10 most relevant features was able to separately identify the type of attack with a performance of at least 90% in the F-measure. We conclude that it is possible to obtain and rank a subset of the most relevant features that characterize the intrusion pattern in the network traffic in order to support the decision of how many features to include during runtime under a real network environment.","PeriodicalId":431144,"journal":{"name":"11th International Conference of Pattern Recognition Systems (ICPRS 2021)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121612040","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: 2

Automatic Classification of Clinical Cases Using Deep Learning Techniques 基于深度学习技术的临床病例自动分类

11th International Conference of Pattern Recognition Systems (ICPRS 2021) Pub Date : 1900-01-01 DOI: 10.1049/icp.2021.1439

B. Cataldo-Vivar, H. Allende-Cid, R. Alfaro

引用次数: 0

Multimodal prediction of aggressive behavior occurrence using a decision-level approach 基于决策层方法的攻击行为多模态预测

11th International Conference of Pattern Recognition Systems (ICPRS 2021) Pub Date : 1900-01-01 DOI: 10.1049/icp.2021.1463

A. Reyes, J. Rudas, Cristian Pulido, L. Chaparro, Jorge Victorino, L. A. Narváez, Darwin Martínez, Francisco Gómez

{"title":"Multimodal prediction of aggressive behavior occurrence using a decision-level approach","authors":"A. Reyes, J. Rudas, Cristian Pulido, L. Chaparro, Jorge Victorino, L. A. Narváez, Darwin Martínez, Francisco Gómez","doi":"10.1049/icp.2021.1463","DOIUrl":"https://doi.org/10.1049/icp.2021.1463","url":null,"abstract":"Traditionally, aggressive behavior incidents have not been considered as a serious crime, but in some contexts such as Bogotá city, this type of behavior caused 70% of the reported personal injuries and homicides in 2017–2018. This phenomenon is a concern for modern cities decision-makers who require predictive models to mitigate aggressive behavior occurrence. There are different source data that can be used to model and predict aggressive behavior, for instance, legal complaints, police penalties and emergency call datasets. In this paper, we propose a decision-level data fusion to combine the prediction of the different aggressive behavior sensors and improve the model predictive capacity. Results suggest that decision-level data fusion using average and max operators improves hotspots hit rates but leads to higher mean squared errors between predicted and real events maps. A texture feature analysis over the predicted maps also revealed that maps generated using the decision-level approach have relatively high entropy, and lower energy and homogeneity values.","PeriodicalId":431144,"journal":{"name":"11th International Conference of Pattern Recognition Systems (ICPRS 2021)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131396243","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

Covid-19 Detection from Chest X-Ray Images Using Deep CNN Architectures with Transfer Learning 基于迁移学习的深度CNN架构从胸部x射线图像中检测Covid-19

11th International Conference of Pattern Recognition Systems (ICPRS 2021) Pub Date : 1900-01-01 DOI: 10.1049/icp.2021.1462

R. Chelghoum, A. Ikhlef, S. Jacquir

{"title":"Covid-19 Detection from Chest X-Ray Images Using Deep CNN Architectures with Transfer Learning","authors":"R. Chelghoum, A. Ikhlef, S. Jacquir","doi":"10.1049/icp.2021.1462","DOIUrl":"https://doi.org/10.1049/icp.2021.1462","url":null,"abstract":"The novel coronavirus COVID-19 first appeared in China at the end of 2019 and was subsequently classified as a world pandemic. At the time of writing, the number of affected persons is 52,331,462 persons and the number of deaths is 1,287,966 deaths. The most used screening methods of COVID-19 is Reverse-Transcription Polymerase Chain Reaction (RT-PCR) test. The number of RT-PCR test kits available is limited because of the increasing number of cases. Some people with COVID-19 have difficulty breathing and their lungs are damaged. Consequently, radiologists utilized Chest X-Ray images to detect the damage caused the COVID-19 into the lungs. However, manual detection takes an important time and depends on the radiologist's expertise. Therefore, it is important to implement automatic detection methods to solve this problem. Due to the limitation of data sets containing COVID-19 images and the small number of training data, transfer learning based on Convolutional Neural Networks (CNN) can be a good combination to solve this problem. In this work, we propose two pre-trained CNNs architectures AlexNet and Residual Network (ResNet-50) to detect COVID-19. The two presented architectures are trained to detect COVID-19, normal and pneumonia from Chest X-Ray images using a 10-Fold cross validation method. Our proposed model outperforms the existing methods and yielded a mean classification accuracy of 96,74% with AlexNet and 99,2% with ResNet-50. In the future work, we will increase the number of COVID-19, Normal and Pneumonia images in the datasets to outperform the performance metrics.","PeriodicalId":431144,"journal":{"name":"11th International Conference of Pattern Recognition Systems (ICPRS 2021)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129165399","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

Automatic Generation of Web Advertising Layouts: A Synthetic Dataset and a Deep Learning Baseline Model 网络广告布局的自动生成:一个合成数据集和一个深度学习基线模型

11th International Conference of Pattern Recognition Systems (ICPRS 2021) Pub Date : 1900-01-01 DOI: 10.1049/icp.2021.1443

R. Carletto, H. Cardot, N. Ragot

引用次数: 0

HFCER : Hybrid Fusion for Cultural Event Recognition in Images 图像中文化事件识别的混合融合

11th International Conference of Pattern Recognition Systems (ICPRS 2021) Pub Date : 1900-01-01 DOI: 10.1049/icp.2021.1455

Shivansh Srivastava, Bappaditya Mandal, Anirban Chakraborty

{"title":"HFCER : Hybrid Fusion for Cultural Event Recognition in Images","authors":"Shivansh Srivastava, Bappaditya Mandal, Anirban Chakraborty","doi":"10.1049/icp.2021.1455","DOIUrl":"https://doi.org/10.1049/icp.2021.1455","url":null,"abstract":"Understanding high level semantic concepts in images requires information from various modalities of visual concepts. One such task is recognition of events based on still images, which requires simultaneous reasoning about high level semantic concepts like objects, people, scenes and their interactions. In this work, we explore different strategies to fuse object and scene information in images to aid the task of cultural event recognition. We start with early and late fusion strategies to combine object and scene level information to reason about event classes. To support our hypothesis that early fused models are able to extract complementary object and scene information, we propose the use of guided backpropagation method to visualize image activations. Inspection of image activations gives an essence of object-scene complementarity in case of early fusion which is not observed in the case of late fused models. As extensions to early and late fusion techniques, we propose HFCER, a hybrid fusion framework along with an alternating training scheme. The proposed technique shows improvement over its late and early fusion counterparts. Late fusion of different fusion techniques namely late, early and hybrid fusion shows state of the art results on Chalearn LAP cultural event recognition dataset.","PeriodicalId":431144,"journal":{"name":"11th International Conference of Pattern Recognition Systems (ICPRS 2021)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115397512","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

Automatic Music Transcription dedicated to Chinese Traditional Plucked String Instrument Pipa using Multi-string Probabilistic Latent Component Analysis Models 基于多弦概率潜在成分分析模型的中国传统弹拨乐器琵琶自动谱曲研究

11th International Conference of Pattern Recognition Systems (ICPRS 2021) Pub Date : 1900-01-01 DOI: 10.1049/icp.2021.1460

Yuancheng Wang, Yuhui Huang, Wei Wei, D. Cazau, O. Adam, Qiao Wang

{"title":"Automatic Music Transcription dedicated to Chinese Traditional Plucked String Instrument Pipa using Multi-string Probabilistic Latent Component Analysis Models","authors":"Yuancheng Wang, Yuhui Huang, Wei Wei, D. Cazau, O. Adam, Qiao Wang","doi":"10.1049/icp.2021.1460","DOIUrl":"https://doi.org/10.1049/icp.2021.1460","url":null,"abstract":"The Probabilistic Latent Component Analysis (PLCA) provides a flexible and highly interpretable framework to model a diversity of music features such as note event specific features (e.g. pitch, duration, amplitude, frequency shifting) and higherlevel knowledge like instrument timbre for Automatic Music Transcription (AMT). In this paper, Multi-String PLCA (MSPLCA) is proposed and allows to model the timbre of individual string characterized by different thickness and materials for polyphonic music transcription of pipa, the head of Chinese traditional plucked string instruments, which is barely studied in the Music Information Retrieval (MIR) community. A dataset, composing 9 famous pieces of Chinese folk music and the notelevel annotation, is created with help of musicians and music experts. As a result, MS-PLCA and its 2 variants adapted to the pipa acoustic features reach AMT performance similar to those found in literature for other instrument transcription. Finally, we illustrate the importance of modeled acoustic features over 2 most common playing techniques, vibrato and tremolo reflecting the periodic pitch and amplitude modulation.","PeriodicalId":431144,"journal":{"name":"11th International Conference of Pattern Recognition Systems (ICPRS 2021)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127991045","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: 3