Iterative Manifold Embedding Layer Learned by Incomplete Data for Large-Scale Image Retrieval
文献类型：期刊论文
| 作者 | Xu, Jian1,2 ; Wang, Chunheng2; Qi, Chengzuo1,2; Shi, Cunzhao2; Xiao, Baihua2
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| 刊名 | IEEE TRANSACTIONS ON MULTIMEDIA
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| 出版日期 | 2019-06-01 |
| 卷号 | 21期号:6页码:1551-1562 |
| ISSN号 | 1520-9210 |
| 关键词 | Iterative manifold embedding layer image retrieval incomplete data |
| DOI | 10.1109/TMM.2018.2883860 |
| 通讯作者 | Wang, Chunheng(chunheng.wang@ia.ac.cn) |
| 英文摘要 | Existing manifold learning methods are not appropriate for image retrieval tasks, because most of them are unable to process query images and they have much greater computational cost especially for large-scale database. Therefore, we propose the iterative manifold embedding (IME) layer, of which the weights are learned offline by an unsupervised strategy, to explore the intrinsic manifolds by incomplete data. On the large-scale database that contains 27 000 images, the IME layer is more than 120 times faster than other manifold learning methods to embed the original representations at query time. We embed the original descriptors of database images that lie on manifold in a high-dimensional space into manifold-based representations iteratively to generate the IME representations in an offline learning stage. According to the original descriptors and the IME representations of database images, we estimate the weights of the IME layer by ridge regression. In the online retrieval stage, we employ the IME layer to map the original representation of a query image with an ignorable time cost (2 ms per image). We experiment on five public standard datasets for image retrieval. The proposed IME layer significantly outperforms the related dimension reduction methods and manifold learning methods. Without postprocessing, our IME layer achieves a boost in the performance of state-of-the-art image retrieval methods with postprocessing on most datasets, and needs less computational cost. |
| WOS关键词 | QUERY EXPANSION ; FEATURES |
| 资助项目 | National Natural Science Foundation of China[61531019] ; National Natural Science Foundation of China[61601462] ; National Natural Science Foundation of China[71621002] |
| WOS研究方向 | Computer Science ; Telecommunications |
| 语种 | 英语 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| WOS记录号 | WOS:000469337400017 |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/24398]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_影像分析与机器视觉团队 |
| 通讯作者 | Wang, Chunheng |
| 作者单位 | 1.Univ Chinese Acad Sci, Beijing 100190, Peoples R China 2.Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Inst Automat, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Xu, Jian,Wang, Chunheng,Qi, Chengzuo,et al. Iterative Manifold Embedding Layer Learned by Incomplete Data for Large-Scale Image Retrieval[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2019,21(6):1551-1562. |
| APA | Xu, Jian,Wang, Chunheng,Qi, Chengzuo,Shi, Cunzhao,&Xiao, Baihua.(2019).Iterative Manifold Embedding Layer Learned by Incomplete Data for Large-Scale Image Retrieval.IEEE TRANSACTIONS ON MULTIMEDIA,21(6),1551-1562. |
| MLA | Xu, Jian,et al."Iterative Manifold Embedding Layer Learned by Incomplete Data for Large-Scale Image Retrieval".IEEE TRANSACTIONS ON MULTIMEDIA 21.6(2019):1551-1562. |
入库方式： OAI收割
来源：自动化研究所
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