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检索条件"任意字段=Proceedings of the 2019 8th International Conference on Computing and Pattern Recognition"

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A Unified Framework for Discrete Multi-kernel k-means with Kernel Diversity Regularization 26

A Unified Framework for Discrete Multi-kernel k-means with K...

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26th international conference on pattern recognition / 8th international Workshop on Image Mining - theory and Applications (IMTA)

作者： Lu, Yihang Zheng, Xuan Wang, Rong Nie, Feiping Li, Xuelong Northwestern Polytech Univ Sch Comp Sci Xian Peoples R China Northwestern Polytech Univ Sch Artificial Intelligence Opt & Elect Xian Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Multiple kernel clustering seeks to combine several kernels for boosting the clustering performance. However, most existing MKKM methods fail to evaluate kernel correlation adequately, which may inevitably select highly correlated kernels resulting in kernel redundancy. Besides, most existing methods solve the NP-hard cluster labels assignment task in two stages: first learning the relaxed labels with continuous values and then obtaining the discrete labels via other discretization methods like k-means. this two-stage strategy may result in the loss of information owing to the deviation between the genuine solution and the approximated one. In this work, we present a unified framework for Discrete Multi-kernel k-means with Kernel Diversity Regularization (DMK-KDR). It is capable of penalizing highly correlated kernels through a well-designed matrix-induced regularization, thus allowing for improved diversity and reduced redundancy in kernel fusion. Additionally, it learns both discrete and continuous clustering indicator matrices simultaneously, thereby ensuring the integrity of the discrete solution without over-reliance on k-means or the loss of information. the efficacy of our model has been evaluated in a number of experiments using real-world datasets.

关键词： Correlation Diversity reception Redundancy Boosting pattern recognition Kernel Task analysis

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DSWHAR: A Dynamic Sliding Window Based Human Activity recognition Method 19

DSWHAR: A Dynamic Sliding Window Based Human Activity Recogn...

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2022 IEEE SmartWorld, 19th IEEE international conference on Ubiquitous Intelligence and computing, 2022 IEEE international conference on Autonomous and Trusted Vehicles conference, 22nd IEEE international conference on Scalable computing and Communications, 2022 IEEE international conference on Digital Twin, 8th IEEE international conference on Privacy computing and 2022 IEEE international conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Sun, Li Yang, Xiaodong Hu, Chunyu School of Computer Science and Technology Jinan250353 China Shandong Academy of Intelligent Computing Technology Jinan250101 China Institute of Computing Technology Chinese Academy of Sciences The Beijing Key Laboratory of Mobile Computing and Pervasive Device Beijing100190 China

ISBN: (纸本)9798350346558

Sensory data has been widely used for human activity recognition (HAR), where sliding window (SW) is one of the typical methods to segment continuous signals. Most existing HAR methods select fixed-length sliding windows for different activities. However, with complex and diverse human activities, a single fixed sliding window is not suitable for acquiring features of different activities. Some studies have dynamically adjusted the window size based on the activity of feature difference segmentation, but the continuous occurrence of the activity can lead to an oversized window for segmentation and easily introduce additional noise leading to degradation of classification performance. To tackle the above problem, we propose a novel dynamic sliding window based human activity recognition method, called DSWHAR. In the training phase, DSWHAR firstly explores the relationship between the optimal sliding window length and the signal spectrum of each target activity, then a candidate window set is built for the target activity and multiple classifiers are trained using different windows. During the recognition phase, the latest prediction probability is used to dynamically select a suitable window for the next prediction, and the corresponding classifier is used for the final recognition. We compare DSWHAR with the method with fixed sliding windows and dynamic sliding window algorithm on the MHEALth dataset, respectively, and the experimental results show that DSWHAR not only achieves better overall HAR accuracy but also has unbiased performance on different activities. © 2022 IEEE.

关键词： pattern recognition

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Dynamic Behavior pattern: Mining the Fraudsters in Telecom Network 24

Dynamic Behavior Pattern: Mining the Fraudsters in Telecom N...

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24th IEEE international conference on High Performance computing and Communications, 8th IEEE international conference on Data Science and Systems, 20th IEEE international conference on Smart City and 8th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Li, Dengshi Zeng, Lu Hu, Ruimin Huang, Zijun Liang, Xiaocong Zang, Yilong School of Artificial Intelligence Jianghan University Wuhan China National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan China

ISBN: (纸本)9798350319934

In telecom network fraud, the fraud method of the fraudsters varies significantly in different periods. However, the traditional method of telecom fraud is to train the telecom fraud mining model through the user's long-term telephone data. So they can not detect the new fraud method of fraudsters and can not identify those with only have short-term telephone data. To increase the identification precision of telecom fraudsters, the dynamic changes in social structure and behavior pattern features of fraudsters were studied in this paper. Specifically, from the perspective of social structure change, the social structure of fraudsters changes rapidly over time. In contrast, the social structure of normal users is stable over time. From the perspective of user behavior pattern features, we found that the behavior patterns of fraudsters and normal users were very different. the behavior pattern of fraudsters in the different periods also changes significantly. therefore, a telecom network fraud detection framework based on the social relationship evo-lution model was proposed. We designed the feature extraction method of dynamic telephone social behavior patterns based on time slices. the method combines the features of social structure and behavior patterns of users. And the Telephone Network Fraud Detection(TNFD) model is built using LSTM. We set up a sliding time window to enable the model to identify users with only short-term telephone data. this time window dynamically takes the features of the user's telephone social behavior pattern as the input of the TNFD model. Experiments on real dataset show that the precision of TNFD is 7% higher than that of the latest method. Our research may reference public security organs in telecom fraud cases. © 2022 IEEE.

关键词： Crime

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Modem Myoelectric Control - Is it Time to Change the Algorithmic Focus? 46

Modem Myoelectric Control - Is it Time to Change the Algorit...

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46th Annual international conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Khushaba, Rami N. Samuel, Oluwarotimi W. Al-Timemy, Ali H. Li, Guanglin Transport for Nsw AlexandriaNSW Australia University of Derby School of Computing and Data Science Research Centre Derby United Kingdom University of Baghdad Department of Biomedical Eng. Al-Kkwarizmi College of Eng Baghdad Iraq Chinese Academy of Sciences Shenzhen Institute of Advanced Technology Shenzhen China

ISBN: (纸本)9798350371499

this paper explores the evolving landscape of Electromyogram (EMG) signal analysis, focusing on the growing prominence of deep learning (DL) algorithms for hand, wrist, and finger movement recognition. Such algorithms often come with high computational costs, potentially limiting the clinical translation on resource-limited devices and igniting more research on reduced complexity models. this prompts the question: is it time to shift the algorithmic focus in EMG pattern recognition, given the reported performance of some light-weight traditional or hybrid methods emphasizing synergy between different EMG signals? A comparative study is implemented between state-of-the-art deep learning extension for time series classification, denoted as Random Convolutional Kernel Transform (ROCKET), and simple, yet effective pattern recognition methods tailored to exploit basic forms of EMG signal synergies - Waveform Length Phasors (WLPHASOR), Root-Mean-Squared Phasor (RMSPHASOR), and the proposed novel Multi-Signal Waveform Length (MSWL). Tests are conducted on EMG data from 22 participants performing 11 hand and wrist movements using two EMG armbands (10 and 8 channels, respectively), utilizing the open-source LibEMG toolbox. Preliminary findings suggest that, while DL algorithms exhibit formidable capabilities, the performance gap with traditional EMG feature extraction methods may not be as substantial as anticipated. the observations of this study revealed no significant differences in average accuracies between ROCKET, WLPHASOR, and RMSPHASOR (87% average across participants). Furthermore, MSWL significantly enhances performance to 90%, and the combination of ROCKET+MSWL achieves 91% on average across all subjects. these findings challenge the narrative of DL dominance in EMG pattern recognition, urging a re-evaluation of the algorithmic focus and contributing valuable insights to the debate on information extraction from EMG signals. © 2024 IEEE.

关键词： Electromyography

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8th international conference on Advanced computing and Communication Systems, ICACCS 2022

8th International Conference on Advanced Computing and Commu...

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8th international conference on Advanced computing and Communication Systems, ICACCS 2022

ISBN: (纸本)9781665408165

the proceedings contain 399 papers. the topics discussed include: wavelet based speech enhancement algorithm for hearing aid application;a review of image processing applications based on Raspberry-Pi;pest detection in crops using deep neural networks;kitchen safety and security system for children;intrusion detection system for databases: a hybrid metaheuristic clustering and closed sequential pattern mining approach;diabetes prediction using machine learning algorithms;integration of optimization techniques to improve performance of machine learning system;role of cloud security in big data processing for healthcare system;behavioral analysis of students by integrated radial curvature and facial action coding system using DCNN;an efficient mouse tracking system using facial gestures;automatic detection of white blood cancer from blood cells using novel machine learning techniques;portable smart storage units for agricultural products;and smart access control system for covid safety smart access control system during covid.

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MSIP: Study on multi-source infection pattern mining algorithm in four-dimensional spacetime 19

MSIP: Study on multi-source infection pattern mining algorit...

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作者： Li, Deshun Li, Lingyu Li, Kaiyi Yang, Qiuling Yao, Jiangyuan Tan, Yuyin Hainan University School of Computer Science and Technology Haikou China Hainan University School of Cyberspace Security Haikou China

ISBN: (纸本)9798350346558

the existing contagion patterns lack the mining of dynamic multi-source infection events in real four-dimensional space-time, which reduces the applicability of the current research results. To address the shortcomings and challenges of existing research work, we investigate the multi-source infection pattern mining algorithm for infectious dynamics in four-dimensional space-time. In this paper, the multi-source infection pattern (MSIP) model is first defined in four-dimensional spacetime. the proposed model considers elements that are more accurate to the real environment, including multi-source infection, movement trajectory, and spatio-temporal dimension. the infection pattern mining algorithm based on opportunity (MABO) is proposed under the MSIP model. the result of the experiment demonstrates that MABO performs effectively, which can mine more infected events under multi-source infection conditions. © 2022 IEEE.

关键词： Data mining

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Critical Radical Analysis Network for Chinese Character recognition 26

Critical Radical Analysis Network for Chinese Character Reco...

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26th international conference on pattern recognition / 8th international Workshop on Image Mining - theory and Applications (IMTA)

作者： Luo, Guo-Feng Yin, Hua-Yi Wang, Da-Han Zhang, Xu-Yao Zhu, Shun-Zhi Xiamen Univ Technol Sch Comp & Informat Engn Xiamen 361024 Peoples R China Fujian Key Lab Pattern Recognit & Image Understan Xiamen 361024 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Natl Lab Pattern Recognit NLPR Inst Automat Beijing Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Zero-shot learning is a challenging problem in many tasks due to the lack of training samples of the unseen classes. the radical-based zero-shot Chinese character recognition methods treat Chinese characters as a combination of radicals and structures, and recognize Chinese characters by identifying the radicals and structures contained in them. Current approaches generally treat the contribution of all radicals to Chinese character recognition as the same, and the recognition results rely on the network's ability to recognize radicals and their corresponding position information, ignoring the potential value of radicals themselves in eliminating the uncertainty of Chinese characters. In this paper, we model the problem of radical-based Chinese character recognition as an uncertainty elimination problem and propose a Critical Radical Analysis Network (CRAN) to explore the Ideographic Description Sequence (IDS) information for zero-shot Chinese character recognition. Specifically, we propose a novel method to compute the critical values of radicals based on information theory using the predefined Chinese character IDS dictionary. In recognition, we use an iterative approach to translate the predicted radical sequence to target Chinese characters. that is, the radicals of the predicted sequence are sorted in descending order of the critical value, and then the radicals are continuously selected in this order as the information obtained to eliminate the uncertainty of the Chinese character until the character is recognized. We conduct experiments on the CTW, CASIA-AHCDB, and CASIA-HWDB datasets. the experimental results show that the proposed method improves the ability of recognizing unseen Chinese characters, demonstrating the effectiveness of the proposed method.

关键词： Zero-shot learning Chinese character recognition

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New Applications of morphological methodology for the early detection of cancer 8

New Applications of morphological methodology for the early ...

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8th international conference on computing in Engineering and Technology, ICCET 2023

作者： Santra, Soumen Majumdar, Dipankar Mandal, Surajit Kabasi, Vishakha Dept. of MCA Techno International New Town West Bengal Kolkata700156 India Dept. of CSE RCC Institute of Information Technology Kolkata700015 India Dept. of ECE B.P. Poddar Institute of Management & Technology Kolkata700052 India Lava International Limited Uttar Pradesh Noida201301 India

ISBN: (纸本)9781839539237

Morphological structure defines the basic internal structure of any texture or pattern. the morphological analysis gives the idea of hidden features whether they are present within the target structure or not. For diagnosis and treatment of cancer diseases it is necessary to identify and analyze properly the hidden structure of the affected cell at the beginning stage of metastasis. Here we aim to assess qualitatively the status of carcinoma cells and their progress towards metastasis phase using morphological methodologies such as erosion, dilation, skeletonization, branchpoints, top to hat transformation and end point transformation followed by histogram feature extraction technique. the histogram distribution of the transformed images was found to be an effective tool to indicate metastasis. © the Institution of Engineering & Technology 2023.

关键词： Textures

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TWD: A New Deep E2E Model for Text Watermark/Caption and Scene Text Detection in Video 26

TWD: A New Deep E2E Model for Text Watermark/Caption and Sce...

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26th international conference on pattern recognition / 8th international Workshop on Image Mining - theory and Applications (IMTA)

作者： Banerjee, Ayan Shivakumara, Palaiahnakote Acharya, Parikshit Pal, Umapada Canet, Josep Llados Indian Stat Inst Comp Vision & Pattern Recognit Unit Kolkata India Univ Malaya Fac Comp Sci & Informat Technol Kuala Lumpur Malaysia Univ Autonoma Barcelona Comp Vision Ctr Barcelona Spain

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Text watermark detection in video images is challenging because text watermark characteristics are different from caption and scene texts in the video images. Developing a successful model for detecting text watermark, caption, and scene texts is an open challenge. this study aims at developing a new Deep End-to-End model for Text Watermark Detection (TWD), caption and scene text in video images. To standardize non-uniform contrast, quality, and resolution, we explore the U-Net3+ model for enhancing poor quality text without affecting high-quality text. Similarly, to address the challenges of arbitrary orientation, text shapes and complex background, we explore Stacked Hourglass Encoded Fourier Contour Embedding Network (SFCENet) by feeding the output of the U-Net3+ model as input. Furthermore, the proposed work integrates enhancement and detection models as an end-to-end model for detecting multitype text in video images. To validate the proposed model, we create our own dataset (named TW-866), which provides video images containing text watermark, caption (subtitles), as well as scene text. the proposed model is also evaluated on standard natural scene text detection datasets, namely, ICDAR 2019 MLT, CTW1500, Total-Text, and DAST1500. the results show that the proposed method outperforms the existing methods. this is the first work on text watermark detection in video images to the best of our knowledge.

关键词： Deep learning U-Net FCENet Scene text detection Video text detection Watermark text detection

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ICPRAM 2019 - proceedings of the 8th international conference on pattern recognition Applications and Methods

ICPRAM 2019 - Proceedings of the 8th International Conferenc...

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8th international conference on pattern recognition Applications and Methods, ICPRAM 2019

ISBN: (纸本)9789897583513

the proceedings contain 109 papers. the topics discussed include: planar motion bundle adjustment;deep learning for radar pulse detection;fast non-minimal solvers for planar motion compatible homographies;the growing n-gram algorithm: a novel approach to string clustering;pluggable drone imaging analysis framework for mob detection during open-air events;all together now! the benefits of adaptively fusing pre-trained deep representations;a cellular automata based classification algorithm;automatic information extraction from piping and instrumentation diagrams;annealing by increasing resampling in the unified view of simulated annealing;detecting and tracking surgical tools for recognizing phases of the awake brain tumor removal surgery;stochastic phase estimation and unwrapping;accelerated algorithm for computation of all prime patterns in logical analysis of data;adversarial alignment of class prediction uncertainties for domain adaptation;using recurrent neural networks for action and intention recognition of car drivers;and a fast and efficient method for solving the multiple closed curve detection problem.

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