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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
  • 456 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 

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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