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Scene Inference Using Saliency Graphs With Trust-Theoretic Semantic Information Encoding
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AI
DOI：10.1109/LSP.2024.3508538.png)
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摘要
En 中文
Scene inference refers to the identification of the scene from a given set of scene representations such as images. A saliency graph of a scene contains scene-defining objects along with semantic information between them in the graph representation. Existing methods consider the entire scene with uniform weighting to all semantic information for scene inference, resulting in a suboptimal performance. This letter presents an optimal edge weight estimation using the trust theoretic framework to encode semantic information effectively in saliency graphs. We have utilized the notion of converged global absolute trust in saliency scores of salient objects to compute the weighting of semantic information. Experimental results highlight the efficacy of the proposed method.
Keyword:
Peer-to-peer computing
Semantics
Estimation
Symmetric matrices
Image edge detection
Signal processing algorithms
Inference algorithms
Eigenvalues and eigenfunctions
Visualization
Accuracy
Scene inference
saliency graph
semantic information
trust
graph
P2P
期刊
IF:
9.6
论文数:
1.1W
被引数:
1.7W
机构
引用论文
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OAAI
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