A novel CBIR system with WLLTSA and ULRGA
文献类型：期刊论文
| 作者 | Feng, Lin ; Liu, Shenglan; Xiao, Yao; Hong, Qiao; Wu, Bin
|
| 刊名 | NEUROCOMPUTING
 |
| 出版日期 | 2015 |
| 卷号 | 147期号:0页码:509-522 |
| 关键词 | Dimensionality reduction Local curvature Tangent space CBIR RF |
| 通讯作者 | Feng, L |
| 英文摘要 | 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. |
| 学科主题 | Computer Science |
| 源URL | [http://ir.ia.ac.cn/handle/173211/12587]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_机器人应用与理论组 |
| 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.(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. |
入库方式： OAI收割
来源：自动化研究所
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