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Computational Visual Media

, Volume 4, Issue 2, pp 123–137 | Cite as

Knowledge graph construction with structure and parameter learning for indoor scene design

  • Yuan Liang
  • Fei Xu
  • Song-Hai Zhang
  • Yu-Kun Lai
  • Taijiang Mu
Open Access
Research Article
First Online:
Received:
Accepted:
  • 339 Downloads

Abstract

We consider the problem of learning a representation of both spatial relations and dependencies between objects for indoor scene design. We propose a novel knowledge graph framework based on the entity-relation model for representation of facts in indoor scene design, and further develop a weaklysupervised algorithm for extracting the knowledge graph representation from a small dataset using both structure and parameter learning. The proposed framework is flexible, transferable, and readable. We present a variety of computer-aided indoor scene design applications using this representation, to show the usefulness and robustness of the proposed framework.

Keywords

knowledge graph scene design structure learning parameter learning 
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Notes

Acknowledgements

This work was supported by the National Key R&D Program of China (No. 2017YFB1002604), the National Natural Science Foundation of China (No. 61772298), a Research Grant of Beijing Higher Institution Engineering Research Center, and the Tsinghua–Tencent Joint Laboratory for Internet Innovation Technology.

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© The Author(s) 2018

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Authors and Affiliations

  • Yuan Liang
    • 1
  • Fei Xu
    • 1
  • Song-Hai Zhang
    • 1
  • Yu-Kun Lai
    • 2
  • Taijiang Mu
    • 1
  1. 1.TNList, Department of Computer ScienceTsinghua UniversityBeijingChina
  2. 2.School of Computer Science and InformaticsCardiff UniversityCardiffUK

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