Semantic embedding for indoor scene recognition by weighted hypergraph learning
文献类型：期刊论文
| 作者 | Yu Jun; Hong Chaoqun; Tao Dapeng; Wang Meng |
| 刊名 | SIGNAL PROCESSING
 |
| 出版日期 | 2015 |
| 英文摘要 | Conventional methods for indoor scenes classification is a challenging task due to the gaps between images' visual features and semantics. These methods do not consider the interactions among features or objects. In this paper, a novel approach is proposed to classify scenes by embedding semantic information in the weighted hypergraph learning. First, hypergraph regularization is improved by optimizing weights of hyperedges. Second, the connectivity among images is learned by statistics of objects appearing in the same image. In this way, semantic gap is narrowed. The experimental results demonstrate the effectiveness of the proposed method. (C) 2014 Elsevier B.V. All rights reserved. |
| 收录类别 | SCI |
| 原文出处 | http://www.sciencedirect.com/science/article/pii/S0165168414003612 |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/6686]  |
| 专题 | 深圳先进技术研究院_集成所 |
| 作者单位 | SIGNAL PROCESSING |
| 推荐引用方式 GB/T 7714 | Yu Jun,Hong Chaoqun,Tao Dapeng,et al. Semantic embedding for indoor scene recognition by weighted hypergraph learning[J]. SIGNAL PROCESSING,2015. |
| APA | Yu Jun,Hong Chaoqun,Tao Dapeng,&Wang Meng.(2015).Semantic embedding for indoor scene recognition by weighted hypergraph learning.SIGNAL PROCESSING. |
| MLA | Yu Jun,et al."Semantic embedding for indoor scene recognition by weighted hypergraph learning".SIGNAL PROCESSING (2015). |
入库方式： OAI收割
来源：深圳先进技术研究院
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