A novel CBIR system with WLLTSA and ULRGA

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 机器人理论与应用

	A novel CBIR system with WLLTSA and ULRGA
	Feng, Lin; Liu, Shenglan; Xiao, Yao; Hong, Qiao; Wu, Bin; Feng, L
发表期刊	NEUROCOMPUTING
	2015
卷号	147 期号:0 页码:509-522
摘要	At present, relevance feedback (RF) has been widely applied in content-based image retrieval (CBIR) system. Local Regression and Global Alignment (LRGA) is a novel ranking algorithm used in CBIR system which utilizes RE technique. However, there are some problems in LRGA: (1) for handling the problem of out-of-sample, dimension reduction is used after RF, but it is time-consuming; (2) feature space of images is often assumed to be linear. While, classical manifold learning methods are sensitive to the Gaussian bandwidth parameter of Laplacian matrix and cannot be combined with RF either. To address problems above, this paper proposes a novel CBIR system. Firstly, we calculate the local curvature parameter of manifold utilizing the angle information in subspace to avoid local high curvature problem and then we propose a Warp Linear Local Tangent Space Alignment (WLLTSA) algorithm; furthermore, we propose a U-Local Regression and Global Alignment (ULRGA) ranking algorithm to rank low-dimensional image features. Curvature parameter is used in both WLLTSA and ULRGA to enhance robustness. A large amount of experimental results demonstrate the efficiency of our CBIR system. (C) 2014 Elsevier B.V. All rights reserved.
关键词	Dimensionality Reduction Local Curvature Tangent Space Cbir Rf
学科领域	Computer Science
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/12587
专题	多模态人工智能系统全国重点实验室_机器人理论与应用
通讯作者	Feng, L
推荐引用方式 GB/T 7714	Feng, Lin,Liu, Shenglan,Xiao, Yao,et al. A novel CBIR system with WLLTSA and ULRGA[J]. NEUROCOMPUTING,2015,147(0):509-522.
APA	Feng, Lin,Liu, Shenglan,Xiao, Yao,Hong, Qiao,Wu, Bin,&Feng, L.(2015).A novel CBIR system with WLLTSA and ULRGA.NEUROCOMPUTING,147(0),509-522.
MLA	Feng, Lin,et al."A novel CBIR system with WLLTSA and ULRGA".NEUROCOMPUTING 147.0(2015):509-522.