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Overview of indoor scene recognition and representation methods based on multimodal knowledge graphs

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Abstract

This paper provides a comprehensive overview of multi-modal knowledge graph technology and a three-layer framework for scene recognition. Integrating diverse 3D expertise into a deep neural network enhances scene cognition and knowledge representation. Real-time 3D scene graph construction via feature matching is explored, demonstrating the feasibility of effective scene knowledge representation. Leveraging advanced multimodal knowledge graph and scene recognition, the paper presents a promising avenue for AI-driven scene cognition and construction. It contributes to understanding multi-modal knowledge graph technology’s potential in addressing scene recognition challenges and implications for future advancements. This interdisciplinary work establishes a foundation for intelligent scene analysis and interpretation.

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Funding

Funding projects: National Natural Science Foundation of China (61375084); Natural Science Foundation of Shandong Province (ZR2019MF064).

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  1. School of Information Science and Electrical Engineering, Shandong Jiaotong University, Changqing University Science Park, Jinan, 250357, Shandong, China

    Jianxin Li, Guannan Si, Pengxin Tian & Zhaoliang An

  2. School of Control Science and Engineering, Shandong University, 17923 Jingshi Road, Jinan, 250061, Shandong, China

    Fengyu Zhou

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Correspondence to Guannan Si.

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Li, J., Si, G., Tian, P. et al. Overview of indoor scene recognition and representation methods based on multimodal knowledge graphs. Appl Intell 54, 899–923 (2024). https://doi.org/10.1007/s10489-023-05235-7

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