Semantic embedding for indoor scene recognition by weighted hypergraph learning

	Semantic embedding for indoor scene recognition by weighted hypergraph learning
	Yu Jun; Hong Chaoqun; Tao Dapeng; Wang Meng
刊名	SIGNAL PROCESSING
	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
内容类型	期刊论文
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).