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Parallel Image Texture Feature Extraction under Hadoop Cloud Platform

机译:Hadoop云平台下并行图像纹理特征提取

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With the increasing amount of digital image data, massive image process and feature extraction process have become a time-consuming process. As an excellent mass data processing and storage capacity of the open source cloud platform, Hadoop provides a parallel computing model MapReduce, HDFS distributed file system module. Firstly, we introduced Hadoop platform programming framework and Tamura texture features. And then, the image processing and feature texture feature extraction calculations involved in the process to achieve Hadoop platform. The results which comparison with Matlab platform shows it is less obvious advantage of Hadoop platform in image processing and feature extraction of lower-resolution images, but for image processing and feature extraction of high-resolution images, the time spent in Hadoop platform is greatly reducing, data processing capability the advantages is obvious.
机译:随着数字图像数据量的增加,海量图像处理和特征提取处理已成为一项耗时的过程。作为开源云平台出色的海量数据处理和存储能力,Hadoop提供了并行计算模型MapReduce,HDFS分布式文件系统模块。首先,我们介绍了Hadoop平台编程框架和Tamura纹理功能。然后,通过图像处理和特征纹理特征提取计算过程来实现Hadoop平台。与Matlab平台的比较结果表明,Hadoop平台在低分辨率图像的图像处理和特征提取方面优势不那么明显,但是对于图像处理和高分辨率图像的特征提取而言,在Hadoop平台上花费的时间大大减少了,数据处理能力的优点是显而易见的。

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