Name
Abstract | ||
|---|---|---|
As an important part of multimedia retrieval, researches on 3D model retrieval concentrate on the shape-based retrieval method. It is a promising way to improve retrieval performance by adopting semantic information. At present, semantics of an object is usually represented by several keywords. However, acquiring each 3D model's semantics is very difficult and expensive. To solve the problem, the paper proposes to describe a 3D model's semantics based on its relationship with the semantics of the others, and states an automatic semantic annotation based on noisy user feedbacks. The paper first analyzes the semantic relationship reflected by user feedbacks. Then, the semantic relationship is treated as one 3D model's semantic property and is adopted in clustering to detect semantic groups that is named as semantic community. Thirdly, based on the semantic community, the semantics for models is automatically and efficiently annotated based on semantic keywords of a few 3D models. Finally, a retrieval mechanism with long-term semantic learning ability is proposed. The experiments performed on Princeton Shape Benchmark show that the proposed method achieves good performance not only in semantic clustering and annotation but also in semantic retrieval. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/SKG.2010.32 | SKG |
Keywords | Field | DocType |
multimedia systems,learning (artificial intelligence),information retrieval,semantic group,user feedback,3d model retrieval,automatic semantic annotation,semantic community,semantic clustering,long-term semantic,semantic annotation,feedback,semantic learning,semantic retrieval,semantic relationship,solid modelling,semantic property,multimedia retrieval,semantic keyword,semantic information,shape-based retrieval,solid modeling,computational modeling,learning artificial intelligence,shape,correlation,semantics | Semantic similarity,Semantic integration,Semantic technology,Semantic Web Stack,Information retrieval,Computer science,Explicit semantic analysis,Artificial intelligence,Natural language processing,Probabilistic latent semantic analysis,Semantic computing,Semantic compression | Conference |
ISBN | Citations | PageRank |
978-0-7695-4189-1 | 1 | 0.37 |
References | Authors | |
7 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tianyang Lu | 1 | 1 | 1.04 |
| Shaobin Huang | 2 | 11 | 7.93 |
| Peng Wu | 3 | 41 | 13.09 |
| Yeran Jia | 4 | 1 | 0.37 |