Technology of Graphic & Image
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625-629

Image relativity metric based on classification with DCNN

Wang Huiyong
Xie Chunjie
Zhang Xiaoming
Sun Xiaoling
School of Information Science & Engineering, Hebei University of Science & Technology, Shijiazhuang 050018, China

Abstract

When measuring the similarity between images, the content of the physical features(color layout descriptor, gray histogram descriptor, etc. ) may not be very comprehensive, so it is necessary to refer to the semantic information contained in image vision to measure the relativity between images. This paper proposed a method based on DCNN classification model to measure image correlation. The model was used to bind the semantic label from WordNet, it filtered and expanded the label according to WordNet structure, and used the concept set to calculate image relativity. Compared with the manually determined sample data, the peak value of Pearson correlation coefficient can reach 0.73, which proves that this method has a certain effect in the measurement of image correlation.

Foundation Support

河北省自然科学基金资助项目(F2018208116)
河北省科技计划资助项目(16210312D)
河北省教育厅科研资助项目(ZD2015099)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.04.0487
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 2
Section: Technology of Graphic & Image
Pages: 625-629
Serial Number: 1001-3695(2020)02-068-0625-05

Publish History

[2020-02-05] Printed Article

Cite This Article

王会勇, 谢春杰, 张晓明, 等. 基于DCNN分类的图像相关度度量 [J]. 计算机应用研究, 2020, 37 (2): 625-629. (Wang Huiyong, Xie Chunjie, Zhang Xiaoming, et al. Image relativity metric based on classification with DCNN [J]. Application Research of Computers, 2020, 37 (2): 625-629. )

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  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
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Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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