SimRank similarity preserving projection for shape‐based 3D model auto‐annotation

We present a new dimensionality reduction method, called SimRank similarity preserving projection (SSPP), that finds a subspace preserving semantic similarity among data represented with SimRank similarity on a bipartite graph. The relationship between 3D models and keywords in a tagged 3D model dat...

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Bibliographic Details
Published in:IEEJ transactions on electrical and electronic engineering 2018-02, Vol.13 (2), p.341-342
Main Authors: Tatsuma, Atsushi, Aono, Masaki
Format: Article
Language:English
Subjects:
3D model
Annotations
auto‐annotation
dimensionality reduction
Graphical representations
Similarity
SimRank
Three dimensional models
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Summary:We present a new dimensionality reduction method, called SimRank similarity preserving projection (SSPP), that finds a subspace preserving semantic similarity among data represented with SimRank similarity on a bipartite graph. The relationship between 3D models and keywords in a tagged 3D model dataset is represented with bipartite graph. For shape‐based 3D model auto‐annotation, we try to capture the relationship of tagged 3D models by SSPP. Experimental results show that our method outperforms the baseline and previous methods.
ISSN:1931-4973
1931-4981
DOI:10.1002/tee.22532