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Parallel Image Texture Feature Extraction under Hadoop Cloud Platform

机译:Hadoop云平台下的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:随着数字图像数据的增加,大量图像处理和特征提取过程已成为耗时的过程。作为开源云平台的优秀质量数据处理和存储容量,Hadoop提供了一个并行计算模型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,在实现Hadoop平台的过程中涉及的图像处理和特征纹理特征特征提取计算。与MATLAB平台进行比较的结果表明,Hadoop平台在图像处理和特征提取的较低分辨率图像中的特征提取不太明显优势,但是对于高分辨率图像的图像处理和特征提取,在Hadoop平台中花费的时间大大减少了,数据处理能力优势显而易见。

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