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检索条件"任意字段=Conference on Parallel and Distributed Methods for Image Processing"

共 7827 条记录，以下是1621-1630 订阅

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NetANNS: A High-Performance distributed Search Framework Based On In-Network Computing

NetANNS: A High-Performance Distributed Search Framework Bas...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Penghao Zhang Heng Pan Zhenyu Li Gaogang Xie Penglai Cui Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Purple Mountain Laboratories China Computer Network Information Center Chinese Academy of Sciences China

Approximate nearest neighbor search (ANNS) is the most basic and important algorithm in Database, Machine Learning and other applications. With the expansion of cloud computing, the academia focuses on the study of how to optimize distributed frameworks based on approximate nearest neighbor search such as MapReduce, and Memcached. We implement a new distributed ANNS search framework (NetANNS). The main contributions of NetANNS are to accelerate the data preprocessing with programmable switch, and integrate a variety of efficient ANNS algorithms so that it can choose the most suitable algorithm for each datasets. The experiments show that the search efficiency of NetANNS is about 2x than the common distributed ANNS frameworks which are implemented based on the framework of MapReduce.

关键词： Cloud computing Machine learning algorithms Data preprocessing Switches Nearest neighbor methods Approximation algorithms Database machines

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Performance Analysis of a parallel Denoising Algorithm on Intel Xeon Computer System 13th

Performance Analysis of a Parallel Denoising Algorithm on In...

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13th International conference on parallel processing and Applied Mathematics, PPAM 2019

作者： Lirkov, Ivan Institute of Information and Communication Technologies Bulgarian Academy of Sciences Acad. G. Bonchev bl. 25A Sofia1113 Bulgaria

ISBN: (纸本)9783030432218

This paper presents an experimental performance study of a parallel implementation of the Poissonian image restoration algorithm. Hybrid parallelization based on MPI and OpenMP standards is investigated. The implementation is tested for high-resolution radiographic images on a supercomputer using Intel Xeon processors as well as Intel Xeon Phi coprocessors. The experimental results show an essential improvement when running experiments for a variety of problem sizes and number of threads. © 2020, Springer Nature Switzerland AG.

关键词： image reconstruction

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Choosing Appropriate AI-enabled Edge Devices, Not the Costly Ones

Choosing Appropriate AI-enabled Edge Devices, Not the Costly...

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International conference on parallel and distributed Systems (ICPADS)

作者： Ziyang Zhang Feng Li Changyao Lin Shihui Wen Xiangyu Liu Jie Liu School of Computer Science and Technology Harbin Institute of Technology Harbin China School of Astronautics Harbin Institute of Technology Harbin China Institute for Artificial Intelligence Harbin Institute of Technology Shenzhen China

ISBN: (纸本)9781665408790

Advances in Edge AI make it possible to achieve inference deep learning for emerging applications, e.g., smart transportation and smart city on the edge in real-time. Nowadays, different industry companies have developed several edge AI devices with various architectures. However, it is hard for application users to justify how to choose the appropriate edge-AI, due to the lack of benchmark testing results and testbeds specifically used to evaluate the system performance for those edge-AI systems. In this paper, we attempt to design a benchmark test platform for the edge-AI devices and evaluate six mainstream edge devices that are equipped with different computing powers and AI chip architectures. Throughput, power consumption ratio, and cost-effectiveness are chosen as the performance metrics for the evaluation process. Three classic deep learning workloads: object detection, image classification, and natural language processing are adopted with different batch sizes. The results show that under different batch sizes, compared with traditional edge devices, edge devices equipped with AI chips have out-performance in throughput, power consumption ratio, and cost-effectiveness by 134×, 57×, and 32×, respectively. From system perspective, our work not only demonstrates the effective AI capabilities of those edge AI devices, but also provide suggestions for AI optimization at edge in details.

关键词： Performance evaluation Deep learning Power demand image edge detection AI accelerators Object detection Benchmark testing

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Multi-Depth Learning with Multi-Attention for fine-grained image classification

Multi-Depth Learning with Multi-Attention for fine-grained i...

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International conference on Intelligent Computing and Human-Computer Interaction (ICHCI)

作者： Dai, Zuhua Li, Hongyi Li, Kelong Zhou, Anwei Northwest Normal Univ Sch Comp Sci & Engn Lanzhou Peoples R China

ISBN: (纸本)9781665423168

Compared with the traditional image classification task, fine-grained image classification has the difficulty of small differences between classes and large differences within classes. In view of this difficulty, attention proposal has been widely used in fine-grained image classification. However, traditional attention proposal has to localize first and then processing. Model needs to run step by step and the attention focusing method is single. This paper proposed a model (MAMDL, Multi-Attention-Multi-Depth-Learning) which combines multiple attention mechanisms and multi network parallel learning. The advantage of MAMDL is that it can first learn end-to-end. Secondly, the multiple attention mechanisms can effectively combine four attention mechanisms to improve the network's ability to process local features. Finally, this paper focuses on the attention found in the backbone network, Feature extraction from branch convolution neural networks with different depths enhances the classification performance of the model. The experimental results show that MAMDL outperforms mainstream fine-grained image classification methods on the fine-grained image classification dataset CUB-200, Stanford dogs and Stanford cars.

关键词： attention proposal fine-grained image classification multi-task learning

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distributed Feature Extraction on Apache Spark for Human Action Recognition 14

Distributed Feature Extraction on Apache Spark for Human Act...

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14th International conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Nguyen Anh Tu Thien Huynh-The Wong, Kok-Seng Dinh-Mao Bui Lee, Young-Koo Nazarbayev Univ Dept Comp Sci Nur Sultan Kazakhstan Kumoh Natl Inst Technol ICT Convergence Res Ctr Gumi South Korea Kyung Hee Univ Dept Comp Sci & Engn Yongin South Korea

ISBN: (纸本)9781728154534

Local feature extraction is one of the most important tasks to build robust video representation in human action recognition. Recent advances in computing visual features, especially deep-learned features, have achieved excellent performance on a variety of action datasets. However, the extraction process is computing-intensive and extremely time-consuming when conducting it on large-scale video data. Consequently, to extract video features over big data, most of the existing methods that run on single machine become inefficient due to the limit of computation power and memory capacity. In this paper, we propose the elastic solutions for feature extraction based on the Spark framework. Particularly, exploiting the in-memory computing capability of Spark, the process of computing features are parallelized by partitioning video data into videos or frames and place them into resilient distributed datasets (RDDs) for the subsequent processing. Then, we present the parallel algorithms to extract the state-of-the-art deep-learned features on the Spark cluster. Subsequently, using the distributed encoding, the extracted features are aggregated into the global representation which is fed into the learned classifier to recognize actions in videos. Experimental results on a benchmark dataset demonstrate that our proposed methods can significantly speed up the extraction process and achieve the promising scalability performance.

关键词： Human action recognition local feature deep learning Spark MapReduce

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Norm-Aware Embedding for Efficient Person Search

Norm-Aware Embedding for Efficient Person Search

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IEEE/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Chen, Di Zhang, Shanshan Yang, Jian Schiele, Bernt Nanjing Univ Sci & Technol Sch Comp Sci & Engn Minist Educ PCA LabKey Lab Intelligent Percept & Syst High D Nanjing Peoples R China Jiangsu Key Lab Image & Video Understanding Socia Nanjing Peoples R China Max Planck Inst Informat Saarland Informat Campus Saarbrucken Germany

ISBN: (纸本)9781728171685

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 misalignment. 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： Embeddings

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distributed Storage and Query Method of Satellite image Data based on HBase

Distributed Storage and Query Method of Satellite Image Data...

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Computer Communication and Network Security (CCNS), International conference on

作者： Hongjuan Liu Jiancun Li Wenji Li Wei Huang Zhitao Shao Yuan Zhang ShiGuang Wang ZhaoYing Yang China Aero Geophysical Survey and Remote Sensing Center for Natural Resources Key laboratory of airborne geophysics and remote sensing geology Ministry of nature and resources Beijing China China Aero Geophysical Survey and Remote Sensing Center for Natural Resources Beijing China

With the rapid development of aerospace industry, satellite image data has shown a blowout growth. At present, the annual data reception capacity of a satellite has reached TB level, and the data size of a satellite image can reach about 2GB. This poses a serious challenge. Based on the Hadoop framework, this paper studies the HBase-based satellite image big data solution, and provides three query methods as HBase, Hive, and Impala according to the application scenario.

关键词： Satellites Scalability distributed databases Production Big Data Network security Data processing

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4th International conference on Computational Intelligence and Networks, CINE 2020

4th International Conference on Computational Intelligence a...

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4th International conference on Computational Intelligence and Networks, CINE 2020

ISBN: (纸本)9781728156880

The proceedings contain 21 papers. The topics discussed include: complete neighborhood centrality and its application;breaking CAPTCHA characters using multi-task learning CNN and SVM;an unsupervised approach for gait phase detection;prediction of missing EEG channel waveform using LSTM;localizing oscillatory sources in a brain by MEG data during cognitive activity;interactions within the spatially-distributed cortical neural network during visual attentional tasks;handwritten Gujarati word image matching using autoencoder;and an efficient algorithm to minimize makespan on parallel batch processing machines.

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parallel coordinates visualization in the ELK stack 30

Parallel coordinates visualization in the ELK stack

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30th International conference on Computer Graphics and Machine Vision, GraphiCon 2020

作者： Galkin, Timofei Grigorieva, Maria National Research Nuclear University "MEPhI" Kashirskoe shosse 31 Moscow115409 Russia Scientific Research Computing Center Lomonosov Moscow State University Leninskie Gory 1 p.4 Moscow119991 Russia Moscow Center of Fundamental and Applied Mathematics Leninskie Gory 1 Moscow119991 Russia

Modern large-scale distributed computing systems, processing large volumes of data, require mature monitoring systems able to control and track in resources, networks, computing tasks, queues and other components. In recent years, the ELK stack has become very popular for the monitoring of computing environment, largely due to the efficiency and flexibility of the ElasticSearch storage and wide variety of Kibana visualization tools. The analysis of computing infrastructure metadata often requires the visual exploration of multiple parameters simultaneously on one graphical image. Stacked bar charts, heatmaps, radar charts are widely used for the multivariate visual data analysis, but these methods have limitations on the number of parameters. In this research the authors propose to enhance the capacity of Kibana, adding parallel Coordinates diagram - one of the most powerful method for visual interactive analysis of high-dimensional data. It allows to compare many variables together and observe correlations between them. This work describes the development process of parallel Coordinates as a Kibana plugin, and demonstrates an example of visual data analysis based on the Nginx logs metadata. © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Visualization

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Exploration of the Influence on Training Deep Learning Models by Watermarked image Dataset

Exploration of the Influence on Training Deep Learning Model...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Shiqin Liu Shiyuan Feng Jinxia Wu Wei Ren Weiqi Wang Wenwen Zheng School of Computer Science China University of Geosciences Wuhan China Key Laboratory of Network Assessment Technology CAS Chinese Academy of Sciences Beijing China Guizhou Provincial Key Laboratory of Public Big Data Guizhou University Guiyang China

Deep learning has achieved great success in various applications with the help of a large scale of datasets. As a result, sharing the valuable big data that can be applied to training deep learning models is of essential importance currently. However, how to claim ownership and protect the copyright of image big data during the sharing process is still a vital issue that should be addressed. The application of digital watermarks can protect the copyright of image data, at the same time, it also degrades the image quality at the same time. As for invisible digital watermarks, the higher the watermark embedding intensity causes the greater the host image changes. Therefore, the performance of deep learning models may decline due to using the watermarked training set. In this paper, we evaluate the influences of different embedding intensities of various watermarking algorithms on several mainstream models and conclude how the watermarking intensity affects the model training. Besides, referring to watermarking algorithms that have been proposed, we proposed a novel discrete Fourier transform-based watermarking algorithm that can achieve image copyright protection yet maintain the utility of models.

关键词： Training Deep learning image quality Fourier transforms Watermarking Big Data Copyright protection

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