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Image data model optimization method based on cloud computing
Journal of Cloud Computing ( IF 3.7 ) Pub Date : 2020-06-15 , DOI: 10.1186/s13677-020-00178-7
Jingyu Liu , Jing Wu , Linan Sun , Hailong Zhu

In the current age of data explosion, the amount of data has reached incredible proportions. Digital image data constitute most of these data. With the development of science and technology, the demand for networked work and life continues to grow. Cloud computing technology plays an increasingly important role in life and work. This paper studies the optimization methods for cloud computing image data recognition models. The parallelization and task scheduling of the remote-sensing image classification model SCSRC based on spatial correlation regularization and sparse representation are studied in a cloud computing platform. First, cloud detection technology, combined with the dynamic features of the edge overlap region, is implemented in cloud computing mode. For image edge overlap region detection, the SCSRC method is implemented on a single machine, and the time performance of the method is analysed experimentally, which provides a basis for parallelization research under the cloud computing platform. Finally, the speedup and expansion ratio of the SK-SCSRC algorithm are determined by experiment, and MR-SCSRC and SK-SCSRC are compared. The simulation results show that, compared to previous methods, the method of image edge overlap detection is more accurate and the image fusion is better, which improves the image recognition ability in the overlap region and demonstrates the performance improvement of the MR-SCSRC algorithm under scheduling. This method addresses the shortcomings of Hadoop’s existing scheduler and can be integrated into remote-sensing cloud computing systems in the future.

中文翻译:

基于云计算的图像数据模型优化方法

在当前数据爆炸时代,数据量已达到令人难以置信的比例。数字图像数据构成了这些数据的大部分。随着科学技术的发展,对网络工作和生活的需求持续增长。云计算技术在生活和工作中扮演着越来越重要的角色。本文研究了云计算图像数据识别模型的优化方法。在云计算平台上研究了基于空间相关正则化和稀疏表示的遥感图像分类模型SCSRC的并行化和任务调度。首先,云检测技术结合边缘重叠区域的动态特征,以云计算模式实现。对于图像边缘重叠区域检测,SCSRC方法在单机上实现,并通过实验分析了该方法的时间性能,为云计算平台下的并行化研究提供了依据。最后,通过实验确定了SK-SCSRC算法的加速比和扩展比,并比较了MR-SCSRC和SK-SCSRC。仿真结果表明,与以往的方法相比,图像边缘重叠检测方法更加准确,图像融合效果更好,提高了重叠区域的图像识别能力,证明了在MR-SCSRC算法下的性能提高。排程。该方法解决了Hadoop现有调度程序的缺点,并且可以在将来集成到遥感云计算系统中。实验分析了该方法的时间性能,为云计算平台下的并行化研究提供了依据。最后,通过实验确定了SK-SCSRC算法的加速比和扩展比,并比较了MR-SCSRC和SK-SCSRC。仿真结果表明,与以往的方法相比,图像边缘重叠检测方法更加准确,图像融合效果更好,提高了重叠区域的图像识别能力,证明了在MR-SCSRC算法下的性能提高。排程。该方法解决了Hadoop现有调度程序的缺点,并且可以在将来集成到遥感云计算系统中。实验分析了该方法的时间性能,为云计算平台下的并行化研究提供了依据。最后,通过实验确定了SK-SCSRC算法的加速比和扩展比,并比较了MR-SCSRC和SK-SCSRC。仿真结果表明,与以往的方法相比,图像边缘重叠检测方法更加准确,图像融合效果更好,提高了重叠区域的图像识别能力,证明了在MR-SCSRC算法下的性能提高。排程。该方法解决了Hadoop现有调度程序的缺点,并且可以在将来集成到遥感云计算系统中。这为云计算平台下的并行化研究提供了基础。最后,通过实验确定了SK-SCSRC算法的加速比和扩展比,并比较了MR-SCSRC和SK-SCSRC。仿真结果表明,与以往的方法相比,图像边缘重叠检测方法更加准确,图像融合效果更好,提高了重叠区域的图像识别能力,证明了在MR-SCSRC算法下的性能提高。排程。这种方法解决了Hadoop现有调度程序的缺点,并且将来可以集成到遥感云计算系统中。这为云计算平台下的并行化研究提供了基础。最后,通过实验确定了SK-SCSRC算法的加速比和扩展比,并比较了MR-SCSRC和SK-SCSRC。仿真结果表明,与以往的方法相比,图像边缘重叠检测方法更加准确,图像融合效果更好,提高了重叠区域的图像识别能力,证明了在MR-SCSRC算法下的性能提高。排程。该方法解决了Hadoop现有调度程序的缺点,并且可以在将来集成到遥感云计算系统中。仿真结果表明,与以往的方法相比,图像边缘重叠检测方法更加准确,图像融合效果更好,提高了重叠区域的图像识别能力,证明了在MR-SCSRC算法下的性能提高。排程。这种方法解决了Hadoop现有调度程序的缺点,并且将来可以集成到遥感云计算系统中。仿真结果表明,与以前的方法相比,图像边缘重叠检测方法更加准确,图像融合效果更好,提高了重叠区域的图像识别能力,证明了MR-SCSRC算法在低信噪比下的性能提高。排程。该方法解决了Hadoop现有调度程序的缺点,并且可以在将来集成到遥感云计算系统中。
更新日期:2020-06-15
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