Knowledge Commons of Institute of Automation，CAS
| Iterative Manifold Embedding Layer Learned by Incomplete Data for Large-Scale Image Retrieval | |
Xu, Jian1,2 ; Wang, Chunheng2; Qi, Chengzuo1,2; Shi, Cunzhao2; Xiao, Baihua2
| |
| Source Publication | IEEE TRANSACTIONS ON MULTIMEDIA
 |
| ISSN | 1520-9210 |
| 2019-06-01 | |
| Volume | 21Issue:6Pages:1551-1562 |
| Corresponding Author | Wang, Chunheng(chunheng.wang@ia.ac.cn) |
| Abstract | 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. |
| Keyword | Iterative manifold embedding layer image retrieval incomplete data |
| DOI | 10.1109/TMM.2018.2883860 |
| WOS Keyword | QUERY EXPANSION ; FEATURES |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | National Natural Science Foundation of China[61531019] ; National Natural Science Foundation of China[61601462] ; National Natural Science Foundation of China[71621002] |
| Funding Organization | National Natural Science Foundation of China |
| WOS Research Area | Computer Science ; Telecommunications |
| WOS Subject | Computer Science, Information Systems ; Computer Science, Software Engineering ; Telecommunications |
| WOS ID | WOS:000469337400017 |
| Publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/24399 |
| Collection | 中国科学院自动化研究所 |
| Corresponding Author | Wang, Chunheng |
| Affiliation | 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 |
| Recommended Citation 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. |
| Files in This Item: | There are no files associated with this item. | |||||
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Edit Comment