Research on Intelligent Recognition of Coast Image Features in Distributed System Based on Deep Learning

被引:0
作者
Xing, Zhichao [1 ]
Li, Guangming [1 ]
机构
[1] Shandong Univ, Sch Mech Elect & Informat Engn, Weihai 264209, Peoples R China
关键词
Hadoop; SURF; k-means clustering; sparse representation;
D O I
10.2112/JCR-SI108-006.1
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The traditional content-based image recognition method has the disadvantages of low recognition efficiency and poor precision for coast images. Therefore, a massive image recognition method based on Hadoop is proposed in this paper, which implemented distributed computing for coast digital images. In this paper, the speeded up robust features (SURF) of images were acquired, and the SURF features of similar pictures were clustered via the k-means clustering algorithm. Finally, the image features were quantified by term frequency-inverse document frequency data mining technology, and the image was shaped according to the SURF features input by a user to achieve accurate identification of similar images. The analysis of results showed that the recognition means has superior efficiency and accuracy for identifying massive images, which greatly improved the recognition performance of the image and enhances the robustness of the system.
引用
收藏
页码:26 / 31
页数:6
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