JOURNAL OF LIGHT INDUSTRY

CN 41-1437/TS  ISSN 2096-1553

Volume 34 Issue 3
May 2019
Article Contents
XU Yan and CAO Chunping. Crossing semantic gap by semantic kernel SVM combined with improved EMD[J]. Journal of Light Industry, 2019, 34(3): 77-83. doi: 10.3969/j.issn.2096-1553.2019.03.009
Citation: XU Yan and CAO Chunping. Crossing semantic gap by semantic kernel SVM combined with improved EMD[J]. Journal of Light Industry, 2019, 34(3): 77-83. doi: 10.3969/j.issn.2096-1553.2019.03.009 shu

Crossing semantic gap by semantic kernel SVM combined with improved EMD

  • Received Date: 2018-12-16
  • Aiming at the problem that the semantics relation among texts is not considered and the amount of computation is too large while samples increases in crossing semantic gap methods,a method of crossing semantic gap was proposed based on semantic kernel SVM combined with improved EMD. Firstly, to solve the semantic relation problem among texts, the method constructed the semantic kernel function based on taking semantic relations of text features into consideration, extracting text features coexisting with images and combining HowNet common ontology repository.Then the semantic kernel function was embedded into the SVM to classify keywords for obtaining best candidate words. Secondly, the algorithm improved EMD with best decreasing matrix to cut down the amount of computation. The experiment result showed that the method proposed takes full consideration of semantic relation in the texts related,the annotation accuracy rate was obviously higher than the other 3 methods and the annotation time was cut down to 1/5 of before.
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